Artificial Intelligence for Predictive Maintenance Applications: Key Components, Trustworthiness, and Future Trends

Aysegul Ucar

Mehmet Karakose

2 and

Necim Kırımça

Submission received: 30 November 2023 / Revised: 14 January 2024 / Accepted: 18 January 2024 / Published: 20 January 2024

Abstract

Predictive maintenance (PdM) is a policy applying data and analytics to predict when one of the components in a real system has been destroyed, and some anomalies appear so that maintenance can be performed before a breakdown takes place. Using cutting-edge technologies like data analytics and artificial intelligence (AI) enhances the performance and accuracy of predictive maintenance systems and increases their autonomy and adaptability in complex and dynamic working environments. This paper reviews the recent developments in AI-based PdM, focusing on key components, trustworthiness, and future trends. The state-of-the-art (SOTA) techniques, challenges, and opportunities associated with AI-based PdM are first analyzed. The integration of AI technologies into PdM in real-world applications, the human–robot interaction, the ethical issues emerging from using AI, and the testing and validation abilities of the developed policies are later discussed. This study exhibits the potential working areas for future research, such as digital twin, metaverse, generative AI, collaborative robots (cobots), blockchain technology, trustworthy AI, and Industrial Internet of Things (IIoT), utilizing a comprehensive survey of the current SOTA techniques, opportunities, and challenges allied with AI-based PdM.

1. Introduction

The maintenance of the systems has recently become increasingly important for enhancing product efficiency and continuity. Different varieties of system maintenance exist, such as reactive, planned, proactive, and predictive [1]. Figure 1 summarizes them. Reactive maintenance only solves the issue when the system breaks down or malfunctions. The malfunction becomes apparent, and then the repairing steps are applied. Planned maintenance is previously scheduled to perform regular inspections and maintenance tasks at predetermined intervals to prolong the system’s life and reduce repair costs, regardless of whether the system has shown failure signs. Predictive maintenance (PdM) is an approach applying advanced analytics on the obtained data from multiple sensors to predict when the system tends to fail and organize the maintenance tasks accordingly to optimize maintenance intervals, reduce malfunction time, and enhance the system’s reliability.

PdM has shown significant growth and advancements. Most recently, low-cost sensors have been developed, and new real-time condition monitoring systems have been successfully used to obtain big data. These developments, expert algorithms, and expert human experience brought considerable developments and progress in predictive maintenance. Current efforts are given to developing new multivariate statistical models and expert algorithms to improve predictions’ accuracy and reduce labor costs [1,2,3,4,5,6,7,8,9,10,11]. Reaching next-step autonomy in a robotics system is possible thanks to sophisticated artificial intelligence (AI)-based algorithms, models, and expertise. Moreover, the potential AI-based PdM reduces costs and boosts efficiency and safety. Hence, the researchers pay special attention to AI models and techniques to improve the autonomy and adaptability of robotic systems in complex and dynamic industrial environments [12,13,14,15,16,17,18,19,20,21,22].

AI has been successfully employed in the automotive, manufacturing, energy, aerospace, and transportation industries by making real-time predictions and estimations of malfunctions and anomalies on big datasets [23]. It has been demonstrated that AI-based techniques, including machine learning and deep learning, exhibit improved performance and accuracy at PdM utilizing remaining useful life (RUL), fault diagnosis, and predictive maintenance scheduling [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43]. Using AI brings some challenges, such as transparency, explainability, system integration, and ethical issues [44], leading to explainable artificial intelligence (XAI) [45]. Figure 2 shows the general AI cover. This paper aims to comprehensively survey the current state-of-the-art techniques, challenges, and opportunities exhibited by AI-based PdM, focusing on key components, trustworthiness, and future trends. The most recent outcomes and innovations in the field are discussed in this paper by suggesting directions for further investigation. Finally, it gives some insights into the most recent research and advancements in the subject and assists in suggesting prospective areas for future research by providing a thorough overview of the existing literature.

Research Questions

This study examines current advances in AI-based PdM, particularly emphasizing next-generation autonomy. The following research inquiries are what this review is based on:

What are the state-of-the-art (SOTA) PdM methods? Regarding accuracy, cost-effectiveness, and scale, what are their advantages?

What are the advantages of AI-based PdM techniques over traditional techniques regarding performance and cost-effectiveness?

How can testing and validation of AI-based PdM systems be effectively conducted in real-world scenarios?

Taking in the research questions, the contributions of this review are (i) the description of the main components of AI-based PdM systems, (ii) the analysis of the SOTA methods in PdM, (iii) comparison of the AI-based PdM with traditional approaches, (iv) investigation of the challenges and limitations of AI-based PdM, (v) assessment of transparency and explainability in AI-based PdM, (vi) integration of AI into existing PdM systems and workflows, (vii) investigation of ethical issues related to AI-based PdM, (viii) enhancing human–machine interaction in the AI-based PdM systems, (ix) effective testing and validation of AI-based PdM systems, and (x) study of the AI-based PdM advances and emerging topics.

The rest of the paper is organized as follows: Section 2 comprehensively describes the key components of AI-based PdM. The SOTA for PdM and its enabling technologies are presented in Section 3. Then, Section 4 focuses on transparency and explainability in AI-based PdM. The challenges and limitations of using AI for PdM autonomy are highlighted in Section 5. Section 6 presents recent advances and future trends in AI-based PdM. Conclusions are given in Section 7.

2. Key Components in AI-Based Predictive Maintenance

AI-based PdM can be fundamentally broken down into six distinct components: data preprocessing, AI algorithms, decision-making modules, communication and integration, and user interface and reporting, as shown in Figure 3. This section briefly discusses each component to understand how they work together to enable AI-based PdM.

Sensors: Sensors are the frontline data collectors in a PdM system. These specialized devices are strategically placed on equipment and machinery to continuously monitor various parameters, such as temperature, pressure, vibration, and more. Sensor data provides real-time insights into equipment health and forms the foundation for predictive maintenance analysis.

Data Preprocessing: Raw data obtained from sensors often contains noise and inconsistencies. Data preprocessing is the initial step in preparing the data for analysis. It includes data cleaning, normalization, and missing data handling. High-quality data are essential for accurate PdM modeling.

AI Algorithms: AI algorithms, including machine learning and deep learning techniques, are the brain of the PdM systems. The algorithms analyze the data to identify the most important features relating to possible failures. They learn from historical data to predict equipment failures, anomalies, and RUL.

Decision-Making Modules: The insights and predictions generated by the AI algorithms are processed by decision-making modules. These modules are responsible for determining when maintenance actions are needed. They can recommend preventive or corrective maintenance tasks, schedule maintenance, and trigger alerts to maintenance teams when necessary.

Communication and Integration: Communication and integration ensure that the insights generated by the system are effectively translated into action. This component involves interactions with various stakeholders, including maintenance personnel and management. Furthermore, integration with enterprise systems such as ERP and asset management software aligns predictive maintenance with broader organizational goals.

User Interface and Reporting: To make these insights accessible to maintenance staff and decision makers, user interfaces and reporting tools are essential. The tools make it easier for users to understand complex data patterns and make informed decisions by providing data visualization, dashboard, and reporting capabilities. Data visualization tools and dashboards communicate data insights and forecast information to maintenance teams and decision makers. Visual aids help understand complex data patterns and make informed decisions.

The following three data-related units are added to the advanced AI methods to obtain resilient, reliable, secure, and highly stable results using AI-based PdM in complex and dynamic environments.

Sensor data and the Internet of Things (IoT) integration: Integrating IoT and sensor technology is pivotal in next-step autonomy for PdM tasks. The IoT sensors are strategically placed in equipment and machines to monitor their condition in real-time continuously.

Data integration: Data integration combines data from various sources, including historical maintenance records, real-time sensor data, external factors (e.g., weather), and production schedules. This holistic view of equipment health enhances decision making.

Digital twins: Digital twins create virtual replicas of physical assets, facilitating real-time simulation and monitoring. AI systems monitor these digital twins, identifying performance irregularities and recommending optimal maintenance strategies before any physical equipment is adversely affected.

Edge and cloud computing: Edge computing proceeds closer to the data source through IoT sensors for real-time analysis rather than in a centralized data center, which reduces latency and enables real-time analysis. Cloud computing stores and manages enormous amounts of data to analyze historical events.

3. State-of-the-Art Techniques for Predictive Maintenance

SOTA consists of (i) the AI-based PdM approaches, including machine learning algorithms, deep learning, statistical control, and statistical modeling, (ii) data-driven approaches, including big data analytics, data mining, data visualization, and predictive data analysis, (iii) vibration and thermal analysis, (iv) augmented reality (AR), virtual reality (VR), mixed reality (MR), and their extended versions, and digital intelligent assistants, (v) methods based on prescriptive maintenance, (vi) edge and cloud computing, IoT, federated learning, and blockchain, (vii) energy-based methods, and (viii) methods based on prognostics and health management (PHM) [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131]. This section reviews some PdM studies using these approaches. Each paper has been evaluated through the application and the proposed approach.

PdM applications employ all AI approaches, including the classification and/or regression problem type of the supervised learning approach, the clustering problem type of the unsupervised learning approach, or a problem type relating to the reinforcement learning (RL) approach to analyze the large volume of data obtained from real-time condition monitoring systems. The approaches have presented magnificent contributions to PdM tasks [23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52]. Deep neural networks (DNNs) covering CNNs, RNNs, and LSTMs have been used to generate the proposed predictive maintenance strategies and algorithms and improve their prediction accuracy [53,54,55,56,57,58,59,60,61,62,63,64]. In these works, the networks have been applied to extract the significant features from raw sensor data, including images or the data in the form of time series, and to detect, recognize, or predict sudden and expected changes in the system according to the features.

Machine learning algorithms have been successfully applied to many PdM applications [65,66,67,68,69]. Algorithms such as feedforward neural networks (FNNs), decision trees (DT), random forests (RF), and support vector machines (SVMs) have been used to classify sensor data.

Refs. [70,71,72,73,74,75,76,77,78,79,80,81,82,83,84] have shown various works applying statistical and probabilistic modeling approaches such as hidden Markov models (HMMs), Bayesian networks (BNs), Gaussian mixture models (GMMs), extreme gradient boosting (XGBoost), Density-based spatial clustering (DBSC), principal component analysis (PCA), and K-means to PdM tasks. Moreover, they introduced different DNN models, such as LSTM and autoencoders, for the tasks. They have identified the deterioration event by analyzing an interdependent relationship among the data collected from multi-sensors and predicted possible future failures.

In [85,86,87,88,89,90,91,92,129], data-driven approaches, including big data analytics relating to data from various sources, including sensor data, historical records, external factors, and data mining, have been used to improve the accuracy and comprehensiveness of PdM systems. Significantly, interactive and intuitive data visualization tools have contributed to quickly understanding the equipment’s health and making informed decisions.

In [93,94,95,96,97,98,99], vibration analysis is a widely used method of PdM. The analytical method uses sensors to measure the vibrations of machinery and identify possible problem areas, such as bearing failure or misalignment on various machines, including motors, pumps, and gearboxes.

Thermal imaging is another widely known technique for PdM [100,101,102,103,104]. The technique uses infrared cameras to perceive potential problems, such as overheating, breakdown, friction, and energy inefficiencies, on a wide range of equipment, including electrical panels, transformers, and motors, by measuring the temperature of equipment.

In [105,106,107,108], the technologies of AR, VR, MR, digital intelligent assistant, digital twin, and IoT sensors have been leveraged. The IoT sensor has enabled real-time data collection from various sensors attached to equipment in PdM. The technologies of AR, VR, and MR increase the capabilities of maintenance technicians with visual guidance, remote assistance, real-time information, and a virtual view of equipment status to perform PdM, improving efficiency and accuracy. Ref. [109] showed an approach for maintenance experts and operators to interact with a PdM system by AI intelligent assistant through natural language processing (NLP) and user feedback about the success of maintenance interventions.

In [110,111,112,113,114,115], PdM is realized through prescriptive maintenance. It provides specific recommendations for maintenance actions going beyond prediction. It includes detailed instructions for technicians on what steps to take. This approach predicts when maintenance is needed and recommends the most effective and efficient maintenance actions.

Refs. [116,117,118,130,131] have used edge computing and cloud computing. Edge computing is used to proceed closer to the data source through IoT sensors for real-time analysis rather than in a centralized data center, which reduces latency and enables real-time analysis. To analyze historical events, cloud computing stores and manages enormous amounts of data.

In [119,120,121], federated learning, blockchain, and industrial IoT have been used. Federated learning allows multiple parties to train a machine learning model collaboratively without sharing their data. Blockchain ensures data integrity, while industrial IoT sensors provide real-time equipment condition data. It may be indicative of problems if unusual energy consumption is observed. In [122,123,124,125], PdM has been provided by analyzing their energy consumption patterns to gain information on the health of such devices.

In [126,127], PdM has been carried out by focusing on PHM. PHM techniques take a different approach to assessing equipment health from predicting failures. Advanced modeling and data analysis are generally included in these methods, enabling further RUL estimation of an asset and providing valuable insight into maintenance planning. Ref. [128] shows an example of a predictive analytics software platform for PdM. In order to make implementing PdM easier, numerous software platforms and tools have been developed. Premade models and features for data integration are frequently included in these platforms.

This article reviews real-world simulation and experimental applications in tables, while some utilize benchmark datasets. Table 1 describes the benchmark datasets such as the National Aeronautics and Space Administration (NASA) Turbofan [132,133], PHM 2008 [134], NASA Ames Milling [135], NASA Bearing Dataset [136], CWRU Bearing [137], FEMTO Ball Bearing [138], Roll Bearing [139], Backblaze [140], PAKDD2020 Alibaba AI OPS [141], NASA Ames Prognostics [142], Lithium-ion Battery of University of Maryland [143], MOSFET Thermal Overstress Aging [144], MAFAULDA [145], Microsoft Azure PdM [146], GEFCOM [147], and The UCI SECOM [148] in multivariate time-series forms commonly employed in PdM applications.

Table 2 shows a taxonomy for PdM applications in different problem types/approaches from various industries. The second column of Table 2 mainly indicates a class name relating to the repair and maintenance activities from [149]. Table 3 summarizes the study numbers in Table 2. As seen in Table 3, the electrical equipment class of the repair and maintenance field is the most popular in AI and maintenance, with 30 studies focused on this field. The machinery category is an essential topic with ten studies and attracts wide attention in this field. The classes of electronic and optical equipment and fabricated metal products attract attention with nine and eight studies, respectively.

The transport equipment category in Table 3 is noted as an essential field with six studies. The other machinery and equipment category is also notable with five works. On the other hand, the categories computers and communication equipment, industrial machinery and equipment, and buildings and other structures have a lower level of interest, with one study each, respectively. As a result, industries such as electrical equipment, machinery, electronic and optical equipment, and fabricated metal products are popular areas for AI and maintenance. On the other hand, computers and communication equipment, industrial machinery and equipment, and buildings and other structures categories have received more limited attention. These results are essential guidance for directing future research and development efforts.

On the other hand, manufacturing industries, including automotive, aircraft, semiconductor, machine tools, gearbox, pharmaceutical, electronics, steel, robotics, industrial robotics, air conditioning, and industrial robots, are the industry sectors receiving the most attention in AI and maintenance, with 22 studies. This wide-ranging industry shows a significant research focus as it includes a variety of subsectors.

The industries of ‘electrical and electronics’ and ‘electronics’ also have attracted attention with 13 studies. Technological advances and maintenance practices have been reflecting significant interest in this industry. The transport, automotive, high-speed railway, autonomous vehicles, railways, and rail transport industries represent an important research area with ten studies. The combination of subsectors such as automotive, high-speed rail, and autonomous vehicles shows a wide range of topics covering various aspects of this industry. The industries of ‘metals and plastics’ and ‘metal and metallurgy’ have five studies. The energy and sustainability, nuclear power, nuclear energy, renewable energy, and wind energy industry is also a prominent field with five studies and covers a wide range of topics related to energy sustainability.

Information technology, cloud services and data centers, electric vehicle battery technology, medical, medical devices and healthcare services, large service management, building and construction, hydraulics, and architecture, engineering, construction, and facility management industries are important industries represented with two studies. A limited number of studies represent infrastructure, pumping systems, marine, textile, logistics and parcel delivery, steel strip processing, chemical industry, aviation, and home energy management.

In addition, Table 3 also shows different PdM tasks and study numbers. RUL, cost, and charge estimation tasks have attracted attention with 11 studies. These tasks cover strategically essential issues such as estimating the useful life of equipment and determining maintenance costs. Fault detection tasks are a vital topic, with 12 studies focusing on the early detection and prevention of malfunctions that may occur in systems. The condition and vibration monitoring task has attracted the attention of nine studies. The anomaly detection task is a distinct area with four studies focusing on detecting unexpected situations and abnormal behavior. Other tasks are represented by only one study each and seem to concentrate on more specific topics. These tasks include product lifecycle management, component fatigue strength prediction, PdM by a digital intelligent assistant, blockchain framework for PdM, and a big data analytics framework. As a result, basic tasks such as failure prediction, RUL estimation, fault detection, and anomaly detection are popular areas of AI and maintenance. In contrast, other tasks are more specialized and focus on specific topics.

Although PdM tasks often appear to be primarily classification and regression problems in Table 3, the presence of unknown events and the general nature of the data with ambiguous labels clearly shows the importance of unsupervised learning, reinforcement learning, and statistical and probabilistic applications in PdM applications.

In addition, Table 2 includes some PdM tasks without using AI. Ref. [89] includes production quality prediction in the textile industry. Refs. [101,102,103] use thermal imaging technology in railways, medical, and hydraulics industries. Ref. [110] applies data management, predictive data analytic toolbox, recommender and decision support dashboard, and semantic-based learning and reasoning for PdM in the automotive manufacturing industry. Ref. [114] introduces the optimal maintenance strategy applying reliability analysis, sensitivity analysis, and a continuous stochastic process in the rail transport industry. Ref. [115] applies a discrete-event simulation framework for post-prognostic decisions in the aviation industry. Ref. [128] introduces a predictive analytics software platform in the manufacturing industry. Moreover, Ref. [91] includes quality management in the vinyl flooring industry using big data analytics and optimization and edge computing without having a PdM task, but it is related indirectly.

Figure 4 and Figure 5 show the number of studies carried out using different machine learning methods such as FNN, CNN, LSTM, autoencoder, SVM, RNN, fuzzy, RF, K-means, decision trees, rule-based intelligence system, DR, HMM, Bayes, ELM, and gradient boosting in Web of Science and Google Scholar for 2018 to 2023, respectively. Total study numbers in Web of Science and Google Scholar have been determined as (67, 104, 149, 213, 296, and 255) and (4406, 6506, 10,498, 16,638, 22,619, and 28,069) over 2018–2023, respectively. Both the results in Google Scholar and Web of Science clearly show that the fusion of PdM and AI will continue over the years. Since 2019, there has been a notable increase in the use of deep learning methods, specifically LSTM, CNN, and autoencoder, with a particularly pronounced surge in the utilization of DRL in the last two years. Additionally, SVM has maintained its relevance since 2018, and its usage has continued to grow. Another remarkable result has been observed in decision trees. The use of decision trees has shown a consistent upward trend since 2018. Notably, there has been a visible increase in the adoption of fuzzy and rule-based methods. Moreover, HMM, Bayes, gradient boosting, and FNN also exhibit a growing trend in usage since 2018. Even these escalating values in fundamental AI methods underscore the inevitability of AI becoming indispensable in future research endeavors. All the mentioned subjects show current relevance and an exponential increase has emerged, which signifies a robust and growing interest in these topics, indicating that AI will be indispensable in future studies. As a result, the figures exhibit an evolving landscape characterized by an increasing breadth of methods employed in PdM applications, suggesting a continuous effort by researchers to explore and integrate diverse machine learning methods, indicating the adaptability of the field and the ongoing quest for innovative approaches.

Figure 6 and Figure 7 show the number of studies carried out relating to four recent advances in PdM, including (PdM, digital twin, and AI), (PdM, IoT, and AI), (PdM, edge and cloud computing, and AI), and (PdM, AR, VR, MR, and AI) in Google Scholar and Web of Science, respectively. The total number of studies relating to the four technological combinations in Web of Science and Google Scholar has been determined as (2, 15, 24, 42, 50, and 40) and (1184, 2102, 3542, 5554, 7684, and 9867) over 2018–2023, respectively. The number of studies has a consistent annual increase. The observed growth proves a rising interest in incorporating advanced technologies into PdM applications and the shifts in the focus of researchers, reflecting evolving priorities in the field. In addition, the numbers indicate that (PdM, digital twin, and AI) and (PdM, IoT, and AI) have been gaining momentum since 2018 and that the use of (PdM, Edge and cloud computing, and AI) and (PdM, AR, VR, MR, and AI) has increased in the past three years and will increase in the future. As a result, the upward direction of the study numbers points to a positive inclination to integrate innovative techniques in PdM. Researchers are encouraged to explore the potential of digital twin, IoT, edge and cloud computing, AR, VR, MR, and AI for PdM applications.

Table 4 compares various AI methods. As observed, deep learning methods exhibit high accuracy and scalability. On the other hand, machine learning methods show higher cost-effectiveness in comparison. When comparing traditional methods with AI approaches, as in Table 5, AI methods outperform in stability and scalability. However, in terms of computational cost, traditional methods are more efficient.

4. Transparency and Explainability in AI-Based Predictive Maintenance

Model interpretability is critical to AI, as it allows us to gain insights into complex models’ black-box nature and build trustworthy models. Various techniques have emerged to show how these models make predictions and decisions. Ref. [150] exhibited two concepts titled explainability and interpretability used in the XAI area. If the model’s design is understandable to a human being, it is considered interpretable. On the contrary, explainability ties into the idea that explanation is a form of interaction between humans and decision makers. Explainability is regarded as a post hoc tool because this specification covers the techniques used to transform an uninterpretable model into an explainable one.

On the other hand, ref. [151] mentioned that if the models are inherently interpretable, they have their own explanations, which align with the model’s calculation. This implies that interpretability inherently includes explainability. In the interpretable model, each step of the decision-making process can be traced. Still, there remains a gap in explaining why this specific sequence of steps was chosen during the decision-making process. Explainable models are interpretable by default, but the reverse is not always true [152], indicating that explainability is a subset of interpretability. In explainable AI systems, it can be challenging to understand how the model reached a decision, but it can grasp the underlying reasoning behind it. XAI takes a broader approach, striving to design inherently transparent and interpretable models for humans.

The interpretability techniques are classified in different ways. One way is to separate them into two branches, post hoc and ad hoc, concerning the training process [153]. Post hoc techniques use external tools to analyze the trained models. The techniques rely on input perturbations. That is why they can provide unreliable results in cases like adversarial attacks, as in [154].

Moreover, they are not concerned about the model’s inner dynamics and the actions to generate different features. Ad hoc techniques modify the model’s inner dynamics to facilitate understanding, and the model comes with some explanations during the training itself. The post hoc techniques require feature analysis, model inspection, saliency maps, proxy models, mathematical analysis, physical analysis, and explanation by using text and case to extract the explanations. They are divided into “model agnostic” and “model specific”. Model-agnostic models also use any model covering neural networks.

On the other hand, model-specific techniques are developed as specific to the model. Some examples of post hoc techniques are local interpretable model-agnostic explanations (LIME) [155] and SHapley Additive exPlanations (SHAP) [156], class activation map (CAM) [157], layer-wise relevance propagation (LRP) [158], and gradient-weighted class activation mapping (Grad-CAM) [159]. Ad hoc methods include approaches to explaining the model in an explicitly understandable manner, using hand-crafted criteria for the selection of features or incorporating heuristics based on current physics [160,161].

Ref. [162] also classified the interpretation techniques as local and global. Local interpretability provides single predictions, whereas global predicting provides interpretation to make new predictions from model features. Local methods such as LIME and SHAP provide model-agnostic means to explain and interpret the predictions generated by a wide range of machine-learning models. Deep learning models, known for their complexity, benefit from techniques like Grad-CAM and attention mechanisms, which offer insights into the input regions that influence their decisions.

Some approaches to achieving model interpretability include techniques such as partial dependence plots (PDP) [163], decision tree visualization [163,164], attention module [165], and feature importance calculation by using methods such as wrapper, filter, embedded, and dimension reduction techniques [166]. PDPs represent a method of showing the relationship among one or several components and the expected outcomes of machine learning models. They give us an insight into how changes affect model predictions for a given feature. The structure of the decision tree, the observed features split, and the way the tree makes decisions based on the input features are displayed in the decision tree visualization.

On the other hand, the attention module contains a mechanism for generating feature weights consisting of parallel hidden layers. Each module part provides a value between 0 and 1, which shall be multiplied by the suitable feature to enter the rest of the network. The module can be visualized. Feature importance helps understand the features with the most weight in the model’s predictions. Wrapper methods are trained with various input features to obtain the best results using heuristic and sequential search algorithms, which is time consuming [167]. Filter methods employ statistical metrics such as the Pearson correlation coefficient, mutual information, and X2 test prior to training to identify the importance of features. The techniques are unrelated to the model or its predictions after training, and they have no interest in interactions between features [168]. Embedded methods work on the subsets of the data along with techniques such as random forest, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, regularization, decision tree, and XGBoost [169,170]. Dimension reduction techniques such as PCA, ICA (individual component analysis), linear discriminant analysis (LDA) reduce the dimensionality of the dataset to obtain a smaller set of principal components that explain most of the variance in the data [171].

In the literature, there are a lot of other methods, such as rule-based systems and sensitivity analysis. Rule-based systems utilize logical rules to describe the decision-making process of a model, making the decision logic explicit and understandable [172,173]. Lastly, sensitivity analysis helps identify the most influential input variables, contributing to a clearer understanding of a model’s predictive behavior. In an era where AI plays an increasingly prominent role, these interpretability techniques and methods are indispensable tools for building trust, improving model performance, and ensuring ethical and accountable AI systems.

Table 6 summarizes interpretable and explainable studies between 2018 and 2023 and their application content. In [174,175,176,177,178,179,180,181,182,183], SHAP, LIME, ELI5, integrated gradients, SmoothGrad, and LRP have been used. Refs. [184,185,186,187,188,189] use the semantic web rule language, rule-based expert systems, fuzzy systems, quantitative association rule mining (QARM), and data-driven sensitivity analysis. In [190,191,192,193,194,195,196,197,198,199,200,201,202,203,204], some techniques and visualizations such as decision trees, graph-based approaches, the attention modules used together with LSTM, generative adversarial networks (GAN), PDPs, and feature importance calculation methods. Refs. [205,206,207,208] give some new algorithms, including interpretability and explainability, such as temporal fusion separable convolutional network, federated learning, HMM, and reinforcement learning.

An extensive application range of PdM consists of a coal crusher operating at the boiler of the real power plant and gantries in a steelworks converter, a transport line in a steelworks converter, the MW load range in a petrochemical plant, a real hybrid bus, hydraulic systems, a modular aero-propulsion system, an intelligent manufacturing system, the water pumping industry, a large gas distribution network, rolling bearings and a rotating machine, PdM for autonomous underwater vehicles (AUV), a water injection pump, wind turbine systems, the IoT-based manufacturing environment, hard disk drives, the turbofan engine, the drilling machine of an automotive manufacturer, solar photovoltaic energy systems, maintenance work orders, manufacturing, and structural health monitoring.

Figure 8 and Figure 9 show numbers relating to PdM and explainable/interpretable AI in Web of Science and Google Scholar for 2018 to 2023, respectively. The total study numbers in Web of Science and Google Scholar are (6, 5, 9, 30, 34, and 18) and (2328, 3058, 4166, 5834, 7350, and 6540) over 2018–2023, respectively. All numbers show significant growth in using both PdM and explainable/interpretable AI over the specified time frame. The increasing number of studies presents a rising interest and emphasis on these subjects and methods. In addition, the numbers emphasize the importance of PdM and the need for AI systems that are transparent and interpretable. The numbers support that using PdM and explainable/interpretable AI will continue to expand and generate a broader shift in the research focus on these areas.

5. Challenges and Limitations of Using AI for PdM Autonomy

Several problems and limitations that need to be overcome to realize AI’s potential benefits are posed by its use for PdM autonomy. Some key challenges and limitations are (i) transparency and explainability, (ii) integration with existing systems and workflows, (iii) data quality and quantity, (iv) the lack of real-world data, (v) the lack of standard evaluation metrics, (vi) ethical considerations, and (vii) effective human–machine interaction.

Transparency and explainability could make understanding and believing the decisions adopted in this system difficult for human operators, leading to mistrust and lack of acceptance among users. All developments about transparency and explainability are given in Section 4. More development should be undertaken to gain the trust of human operators in PdM applications.

The seamless integration into existing systems and workflows requires specialized software and expertise. Moreover, the different types of data formats, communication methods, and protocols create difficulties in integrating AI systems into current systems. There are some approaches for the seamless integration of AI-based PdM systems into current systems and workflows [209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224]. In [209,210,211,212,213,214,215,216,217,218,219,220,221], a modular design has been applied. The design allows the AI-based system to be flexibly integrated with existing systems and workflows and adaptable using standard interfaces and protocols, such as Modbus, Snap7, and OPC-UA, to communicate with existing systems and develop APIs. Refs. [223,224] has leveraged a service-oriented architecture (SOA) for integration. Web services, such as RESTful APIs, to access the AI-based system and message-oriented middleware, such as MQTT, to communicate with existing systems have been applied. Additionally, some researchers have proposed using edge computing and fog computing to integrate AI-based PdM systems into existing systems [219,220,221,222].

Data quality and quantity are crucial for training and validating AI-based PdM models. High-quality data provides accurate predictions. In contrast, the time and costs of gathering and cleaning data may be considerable. In addition, it is essential to have large amounts of data for learning deep learning models, and in certain cases, they may be challenging to obtain [85,86,129,192,210]. AI-based PdM systems should be tested and validated under various conditions, such as different types of equipment, different operating conditions, and different levels of data quality for successful real-world applications. The lack of real-world data makes it difficult to test the system under realistic conditions and to evaluate its performance and accuracy. Researchers have proposed various methods for simulating real-world scenarios to overcome this challenge, such as using virtual environments and testbeds [26,35,37,42,96,109,210,225,226,227,228,229,230,231].

The lack of standard evaluation metrics is another challenge in testing and validating AI-based PdM systems. The shortcoming makes comparing different systems’ performance and evaluating their accuracy and reliability difficult. Researchers have proposed various evaluation metrics to overcome this challenge, such as prediction accuracy, mean squared error, and precision and recall [24,232].

Ethical considerations are essential for AI-based PdM systems to ensure they are robust, reliable, and secure. Additionally, ethical considerations are associated with using AI-based PdM systems, such as transparency and explainability, trust and acceptance among users, integrating AI-based systems with existing systems and workflows, testing, validating, and data privacy and security [221,233,234].

Human–machine interaction is an essential aspect of AI-based PdM as it involves the interaction between human operators and autonomous systems. The goal of human–machine interaction in PdM is to enable human operators to monitor, control, and interact with autonomous systems safely, efficiently, and effectively. One of the critical challenges in HRI for AI-based PdM is the development of intuitive and user-friendly interfaces [235,236]. Researchers have proposed various methods for designing intuitive and user-friendly interfaces to overcome these challenges, such as virtual and augmented reality gestures and NLP [11,19,21,26,48,50,105,106,108,230]. Another challenge in HRI for AI-based PdM is the development of trust and acceptance among human operators. Generally, ML models, especially deep learning models, provide decisions that are too complicated for humans to understand, reducing people’s trust in their predictions. That is why developing simplified and interpretable models is vital [182]. Recent works have proposed to use NLP and, its more powerful version, generative AI, including large language models (LLMs), such as ChatGPT, PaLM, and Llama [237,238,239,240], to predict component failure or service requirements and to analyze historical log data that include equipment performance, environmental conditions, and maintenance schedules [241,242,243,244,245]. Ref. [246] has used NLP, dimension reduction, and clustering techniques for the PdM of an aircraft by using previous reports. A human collaboration with ChatGPT has applied PdM for mobile firefighter turnout gear cleaning in [247]. Ref. [248] has applied GPT and RL to control an HVAC system. Ref. [249] has proposed to leverage ChatGPT in different areas of supply chain management, such as route optimization, predictive maintenance, and order shipment. Ref. [250] has proposed a new language model for network traffic, including PdM in telecommunication. Critical AI training, testing, and diligence methods for PdM in automotive projects have been introduced in [251]. In [252], LLMs have been investigated for the failure mode classification task, an essential maintenance step. The works in [241,242,243,244,245,246,247,248,249,250,251,252] have shown that NLP and LLMs have increased predictive maintenance efficiency and accuracy since they can be constantly updated with real-time equipment data, enabling them to learn the patterns associated with healthy operational functioning.

6. Recent Advances and Future Trends in AI-Based PdM

Recent advances in AI-based PdM have improved the performance and accuracy of predictive maintenance predictions and increased the autonomy and adaptability of machines in complex and dynamic working environments. Some recent advances in this field include the following:

The field of AI-based PdM has been developing and improving, as shown in Figure 10. The future research topics in the field are given below:

The exponential growth of cyber–physical systems of digital twins, AR, VR, XR, metaverse, and human-driven industrial metaverse solutions to both physical and virtual work environments allows smooth collaboration and communication between employees, machines/robots, and AI.

Development of autonomous maintenance systems that are capable of self-diagnosis, decision making, and proactive interventions without human intervention.

Evolving toward zero-touch maintenance operations where AI systems automate the maintenance process from detection to resolution.

Extraction of actionable insight advancements in AI algorithms to predict failures and provide actionable insights and recommendations for optimal maintenance strategies.

Integrating experiential learning and reinforcement learning techniques to improve AI models based on ongoing data and continuous feedback.

Implementation of blockchain technology for data security to enhance the security and integrity of PdM data, ensuring trust and transparency.

Development of trustworthy AI algorithms and human-centric AI interfaces for better collaboration between AI systems and human operators, facilitating seamless interaction and decision making.

Development of energy-efficient AI-based PdM to minimize resource consumption while maintaining high prediction accuracy.

Development of collaborative robots (cobots), IIoT, edge and cloud computing, and 5G/6G connectivity for next-step PdM autonomy and smart factory that can adjust to shifting circumstances and changing conditions and streamline manufacturing processes.

Development of generative AI models to contribute to the above items. For example, they can provide failure warnings, present encompassing instructions for repair and replacement methodologies, achieve suggestions to optimize energy consumption and cut down the carbon footprint to human operators by simulating the real system and/or analyzing maintenance logs and sensor data, and facilitate better collaboration between automated systems and human operators through natural language communication in automated maintenance planning.

7. Conclusions

This paper has reviewed the recent developments in AI-based PdM, focusing on next-step autonomy in robots. SOTA, challenges, and opportunities associated with AI-based PdM have been analyzed. The ethical considerations, integration, testing, and validation of AI-based PdM in real-world scenarios and human–machine interaction have also been discussed. The potential benefits of AI-based PdM, such as cost savings, increased efficiency, and improved safety, have been highlighted. It has been concluded that PdM is trustworthy thanks to explainable and interpretable AI for human operators. Therefore, AI is the main component of PdM for next-step autonomy in machines, which can improve the autonomy and adaptability of machines in complex and dynamic working environments. Finally, recent advances and future trends, including the use of generative AI models, have been addressed for further improvements and developments of the AI-based PdM.

Author Contributions

Conceptualization, A.U. and M.K. validation, A.U., M.K. and N.K.; formal analysis, A.U., M.K. and N.K.; writing—original draft preparation, A.U. and M.K.; writing—review and editing, A.U., M.K. and N.K.; visualization, A.U. and M.K.; project administration, N.K. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by The Scientific and Technological Research Council of Türkiye—TUBITAK (Grant No. 9210043) under the ECOMAI PENTAEURIPIDES (Grant No. 2021028) roof project and under the Albayrak Makine Elektronik A.Ş internal project. It was also supported by the Scientific Research Projects Coordination Unit of Firat University under project number ADEP.23.08.

Institutional Review Board Statement

Informed Consent Statement

Data Availability Statement

Conflicts of Interest

Necim Kırımça was employed by the Albayrak Makine Elektronik A.S, in Eskisehir, Turkey. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

1. Dalzochio, J.; Kunst, R.; Pignaton, E.; Binotto, A.; Sanyal, S.; Favilla, J.; Barbosa, J. Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges. Comput. Ind. 2020, 123 , 103298. [Google Scholar] [CrossRef]
2. Cho, H. Uncertainty Management in Prognosis of Electric Vehicle Energy System. Ph.D. Dissertation, School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA, 2018. [Google Scholar]
3. Abbasi, T.; Lim, K.H.; Yam, K.S. Predictive maintenance of oil and gas equipment using recurrent neural network. In Proceedings of the Iop Conference Series: Materials Science and Engineering, Jakarta, Indonesia, 21–22 November 2019; p. 012067. [Google Scholar]
4. Zhang, W.; Yang, D.; Wang, H. Data-driven methods for predictive maintenance of industrial equipment: A survey. IEEE Syst. J. 2019, 13 , 2213–2227. [Google Scholar] [CrossRef]
5. Huang, M.; Liu, Z.; Tao, Y. Mechanical fault diagnosis and prediction in IoT based on multi-source sensing data fusion. Simul. Model. Pract. Theory 2020, 102 , 101981. [Google Scholar] [CrossRef]
6. Arena, F.; Collotta, M.; Luca, L.; Ruggieri, M.; Termine, F.G. Predictive maintenance in the automotive sector: A literature review. Math. Comput. Appl. 2021, 27 , 2. [Google Scholar] [CrossRef]
7. Zhang, S.; Liu, C.; Su, S.; Han, Y.; Li, X. A feature extraction method for predictive maintenance with time-lagged correlation–based curve-registration model. Int. J. Netw. Manag. 2018, 28 , e2025. [Google Scholar] [CrossRef]
8. Harris, A.; Yellen, M. Decision-Making with Machine Prediction: Evidence from Predictive Maintenance in Trucking. 2024. Available online: https://adamharris380.github.io/files/HarrisYellen-JMP.pdf (accessed on 19 January 2024).
9. Ahmad, A.A.; Alshurideh, M. Digital Twin in Facility Management Operational Decision Making and Predictive Maintenance. In Proceedings of the International Conference on Advanced Intelligent Systems and Informatics, Cairo, Egypt, 20–22 November 2022; pp. 437–448. [Google Scholar]
10. Achouch, M.; Dimitrova, M.; Ziane, K.; Sattarpanah Karganroudi, S.; Dhouib, R.; Ibrahim, H.; Adda, M. On predictive maintenance in industry 4.0: Overview, models, and challenges. Appl. Sci. 2022, 12 , 8081. [Google Scholar] [CrossRef]
11. Khatri, M.R. Integration of natural language processing, self-service platforms, predictive maintenance, and prescriptive analytics for cost reduction, personalization, and real-time insights customer service and operational efficiency. Int. J. Inf. Cybersecur. 2023, 7 , 1–30. [Google Scholar]
12. Vulpio, A.; Oliani, S.; Suman, A.; Zanini, N.; Saccenti, P. A Mechanistic Model for the Predictive Maintenance of Heavy-Duty Centrifugal Fans Operating With Dust-Laden Flows. J. Eng. Gas Turbines Power 2023, 145 , 011007. [Google Scholar] [CrossRef]
13. Xia, L.; Liang, Y.; Leng, J.; Zheng, P. Maintenance planning recommendation of complex industrial equipment based on knowledge graph and graph neural network. Reliab. Eng. Syst. Saf. 2023, 232 , 109068. [Google Scholar] [CrossRef]
14. Gao, Q.; Yang, Y.; Kang, Q.; Tian, Z.; Song, Y. EEG-based emotion recognition with feature fusion networks. Int. J. Mach. Learn. Cybern. 2022, 13 , 421–429. [Google Scholar] [CrossRef]
15. Ouadah, A.; Zemmouchi-Ghomari, L.; Salhi, N. Selecting an appropriate supervised machine learning algorithm for predictive maintenance. Int. J. Adv. Manuf. Technol. 2022, 119 , 4277–4301. [Google Scholar] [CrossRef]
16. Jiang, Y.; Dai, P.; Fang, P.; Zhong, R.Y.; Cao, X. Electrical-STGCN: An electrical spatio-temporal graph convolutional network for intelligent predictive maintenance. IEEE Trans. Ind. Inform. 2022, 18 , 8509–8518. [Google Scholar] [CrossRef]
17. Del Buono, F.; Calabrese, F.; Baraldi, A.; Paganelli, M.; Regattieri, A. Data-driven predictive maintenance in evolving environments: A comparison between machine learning and deep learning for novelty detection. In Proceedings of the International Conference on Sustainable Design and Manufacturing, Split, Croatia, 15–17 September 2021; pp. 109–119. [Google Scholar]
18. Zhao, J.; Gao, C.; Tang, T. A review of sustainable maintenance strategies for single component and multicomponent equipment. Sustainability 2022, 14 , 2992. [Google Scholar] [CrossRef]
19. Lee, H.; Kang, D.H.; Jeong, S.C. A Study on Industrial Artificial Intelligence-Based Edge Analysis for Machining Facilities. In Emotional Artificial Intelligence and Metaverse ; Springer: New York, NY, USA, 2022; pp. 55–69. [Google Scholar]
20. Sanzana, M.R.; Maul, T.; Wong, J.Y.; Abdulrazic, M.O.M.; Yip, C.-C. Application of deep learning in facility management and maintenance for heating, ventilation, and air conditioning. Autom. Constr. 2022, 141 , 104445. [Google Scholar] [CrossRef]
21. Lv, J.; Li, X.; Sun, Y.; Zheng, Y.; Bao, J. A bio-inspired LIDA cognitive-based Digital Twin architecture for unmanned maintenance of machine tools. Robot. Comput. Integr. Manuf. 2023, 80 , 102489. [Google Scholar] [CrossRef]
22. Ren, Y. Optimizing predictive maintenance with machine learning for reliability improvement. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part B Mech. Eng. 2021, 7 , 030801. [Google Scholar] [CrossRef]
23. Cardoso, D.; Ferreira, L. Application of predictive maintenance concepts using artificial intelligence tools. Appl. Sci. 2020, 11 , 18. [Google Scholar] [CrossRef]
24. Ye, Y.; Yong, Z.; Han, D. Research on key technology of industrial artificial intelligence and its application in predictive maintenance. Acta Autom. Sin. 2020, 46 , 2013–2030. [Google Scholar]
25. Paolanti, M.; Romeo, L.; Felicetti, A.; Mancini, A.; Frontoni, E.; Loncarski, J. Machine learning approach for predictive maintenance in industry 4.0. In Proceedings of the 2018 14th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Oulu, Finland, 2–4 July 2018; pp. 1–6. [Google Scholar]
26. Bordegoni, M.; Ferrise, F. Exploring the intersection of metaverse, digital twins, and artificial intelligence in training and maintenance. J. Comput. Inf. Sci. Eng. 2023, 23 , 060806. [Google Scholar] [CrossRef]
27. Carvalho, T.P.; Soares, F.A.; Vita, R.; Francisco, R.d.P.; Basto, J.P.; Alcalá, S.G. A systematic literature review of machine learning methods applied to predictive maintenance. Comput. Ind. Eng. 2019, 137 , 106024. [Google Scholar] [CrossRef]
28. Traini, E.; Bruno, G.; D’antonio, G.; Lombardi, F. Machine learning framework for predictive maintenance in milling. IFAC-Pap. 2019, 52 , 177–182. [Google Scholar] [CrossRef]
29. Çınar, Z.M.; Abdussalam Nuhu, A.; Zeeshan, Q.; Korhan, O.; Asmael, M.; Safaei, B. Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability 2020, 12 , 8211. [Google Scholar] [CrossRef]
30. Aremu, O.O.; Palau, A.S.; Parlikad, A.K.; Hyland-Wood, D.; McAree, P.R. Structuring data for intelligent predictive maintenance in asset management. IFAC-Pap. 2018, 51 , 514–519. [Google Scholar] [CrossRef]
31. Jimenez, V.J.; Bouhmala, N.; Gausdal, A.H. Developing a predictive maintenance model for vessel machinery. J. Ocean Eng. Sci. 2020, 5 , 358–386. [Google Scholar] [CrossRef]
32. Shukla, B.; Fan, I.-S.; Jennions, I. Opportunities for explainable artificial intelligence in aerospace predictive maintenance. In Proceedings of the PHM Society European Conference, Turin, Italy, 1–3 July 2020; p. 11. [Google Scholar]
33. Theissler, A.; Pérez-Velázquez, J.; Kettelgerdes, M.; Elger, G. Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry. Reliab. Eng. Syst. Saf. 2021, 215 , 107864. [Google Scholar] [CrossRef]
34. Ayvaz, S.; Alpay, K. Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time. Expert Syst. Appl. 2021, 173 , 114598. [Google Scholar] [CrossRef]
35. Liu, Z.; Meyendorf, N.; Mrad, N. The role of data fusion in predictive maintenance using digital twin. In Proceedings of the AIP Conference Proceedings, Depok, Indonesia, 30–31 October 2018. [Google Scholar]
36. Massaro, A.; Selicato, S.; Galiano, A. Predictive maintenance of bus fleet by intelligent smart electronic board implementing artificial intelligence. IoT 2020, 1 , 12. [Google Scholar] [CrossRef]
37. Hosamo, H.H.; Svennevig, P.R.; Svidt, K.; Han, D.; Nielsen, H.K. A Digital Twin predictive maintenance framework of air handling units based on automatic fault detection and diagnostics. Energy Build. 2022, 261 , 111988. [Google Scholar] [CrossRef]
38. Cakir, M.; Guvenc, M.A.; Mistikoglu, S. The experimental application of popular machine learning algorithms on predictive maintenance and the design of IIoT based condition monitoring system. Comput. Ind. Eng. 2021, 151 , 106948. [Google Scholar] [CrossRef]
39. De Benedetti, M.; Leonardi, F.; Messina, F.; Santoro, C.; Vasilakos, A. Anomaly detection and predictive maintenance for photovoltaic systems. Neurocomputing 2018, 310 , 59–68. [Google Scholar] [CrossRef]
40. Lee, S.M.; Lee, D.; Kim, Y.S. The quality management ecosystem for predictive maintenance in the Industry 4.0 era. Int. J. Qual. Innov. 2019, 5 , 1–11. [Google Scholar] [CrossRef]
41. Pech, M.; Vrchota, J.; Bednář, J. Predictive maintenance and intelligent sensors in smart factory. Sensors 2021, 21 , 1470. [Google Scholar] [CrossRef] [PubMed]
42. Mourtzis, D.; Tsoubou, S.; Angelopoulos, J. Robotic Cell Reliability Optimization Based on Digital Twin and Predictive Maintenance. Electronics 2023, 12 , 1999. [Google Scholar] [CrossRef]
43. de Oliveira, R.O.; Coppola, M.; Vermesan, O. AI in Food and Beverage Industry. In Artificial Intelligence for Digitising Industry–Applications ; River Publishers: Roma, Italy, 2022; pp. 251–259. [Google Scholar]
44. AI, H. High-Level Expert Group on Artificial Intelligence ; European Commission: Brussels, Belgium, 2019; p. 6. [Google Scholar]
45. Pashami, S.; Nowaczyk, S.; Fan, Y.; Jakubowski, J.; Paiva, N.; Davari, N.; Bobek, S.; Jamshidi, S.; Sarmadi, H.; Alabdallah, A. Explainable Predictive Maintenance. arXiv 2023, arXiv:2306.05120. [Google Scholar]
46. Zonta, T.; Da Costa, C.A.; da Rosa Righi, R.; de Lima, M.J.; da Trindade, E.S.; Li, G.P. Predictive maintenance in the Industry 4.0: A systematic literature review. Comput. Ind. Eng. 2020, 150 , 106889. [Google Scholar] [CrossRef]
47. Shin, W.; Han, J.; Rhee, W. AI-assistance for predictive maintenance of renewable energy systems. Energy 2021, 221 , 119775. [Google Scholar] [CrossRef]
48. Angelopoulos, J.; Mourtzis, D. An intelligent product service system for adaptive maintenance of Engineered-to-Order manufacturing equipment assisted by augmented reality. Appl. Sci. 2022, 12 , 5349. [Google Scholar] [CrossRef]
49. Wang, J.; Gao, R.X. Innovative smart scheduling and predictive maintenance techniques. In Design and Operation of Production Networks for Mass Personalization in the Era of Cloud Technology ; Elsevier: New York, NY, USA, 2022; pp. 181–207. [Google Scholar]
50. Lalik, K.; Wątorek, F. Predictive maintenance neural control algorithm for defect detection of the power plants rotating machines using augmented reality goggles. Energies 2021, 14 , 7632. [Google Scholar] [CrossRef]
51. Namuduri, S.; Narayanan, B.N.; Davuluru, V.S.P.; Burton, L.; Bhansali, S. Deep learning methods for sensor based predictive maintenance and future perspectives for electrochemical sensors. J. Electrochem. Soc. 2020, 167 , 037552. [Google Scholar] [CrossRef]
52. Divya, D.; Marath, B.; Santosh Kumar, M. Review of fault detection techniques for predictive maintenance. J. Qual. Maint. Eng. 2023, 29 , 420–441. [Google Scholar] [CrossRef]
53. Gianoglio, C.; Ragusa, E.; Gastaldo, P.; Gallesi, F.; Guastavino, F. Online Predictive Maintenance Monitoring Adopting Convolutional Neural Networks. Energies 2021, 14 , 4711. [Google Scholar] [CrossRef]
54. Zonta, T.; da Costa, C.A.; Zeiser, F.A.; de Oliveira Ramos, G.; Kunst, R.; da Rosa Righi, R. A predictive maintenance model for optimizing production schedule using deep neural networks. J. Manuf. Syst. 2022, 62 , 450–462. [Google Scholar] [CrossRef]
55. De Santo, A.; Ferraro, A.; Galli, A.; Moscato, V.; Sperlì, G. Evaluating time series encoding techniques for predictive maintenance. Expert Syst. Appl. 2022, 210 , 118435. [Google Scholar] [CrossRef]
56. Pierleoni, P.; Palma, L.; Belli, A.; Raggiunto, S.; Sabbatini, L. Supervised Regression Learning for Maintenance-related Data. In Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Calabria, Italy, 12–15 September 2022; pp. 1–6. [Google Scholar]
57. Wu, J.-Y.; Wu, M.; Chen, Z.; Li, X.-L.; Yan, R. Degradation-aware remaining useful life prediction with LSTM autoencoder. IEEE Trans. Instrum. Meas. 2021, 70 , 1–10. [Google Scholar] [CrossRef]
58. Souza, R.M.; Nascimento, E.G.; Miranda, U.A.; Silva, W.J.; Lepikson, H.A. Deep learning for diagnosis and classification of faults in industrial rotating machinery. Comput. Ind. Eng. 2021, 153 , 107060. [Google Scholar] [CrossRef]
59. Li, K.; Xiong, M.; Li, F.; Su, L.; Wu, J. A novel fault diagnosis algorithm for rotating machinery based on a sparsity and neighborhood preserving deep extreme learning machine. Neurocomputing 2019, 350 , 261–270. [Google Scholar] [CrossRef]
60. Dong, Z.; Ji, X.; Wang, J.; Gu, Y.; Wang, J.; Qi, D. ICNCS: Internal Cascaded Neuromorphic Computing System for Fast Electric Vehicle State of Charge Estimation. IEEE Trans. Consum. Electron. 2023. [Google Scholar] [CrossRef]
61. Balazy, P.; Gut, P.; Knap, P. Neural classifying system for predictive maintenance of rotating devices. In IOP Conference Series: Materials Science and Engineering ; IOP Publishing: Bristol, UK, 2022; p. 012013. [Google Scholar]
62. Wang, Q.; Bu, S.; He, Z. Achieving predictive and proactive maintenance for high-speed railway power equipment with LSTM-RNN. IEEE Trans. Ind. Inform. 2020, 16 , 6509–6517. [Google Scholar] [CrossRef]
63. Chen, J.C.; Chen, T.-L.; Liu, W.-J.; Cheng, C.; Li, M.-G. Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery. Adv. Eng. Inform. 2021, 50 , 101405. [Google Scholar] [CrossRef]
64. Jiang, Y.; Dai, P.; Fang, P.; Zhong, R.Y.; Zhao, X.; Cao, X. A2-LSTM for predictive maintenance of industrial equipment based on machine learning. Comput. Ind. Eng. 2022, 172 , 108560. [Google Scholar] [CrossRef]
65. Shamayleh, A.; Awad, M.; Farhat, J. IoT based predictive maintenance management of medical equipment. J. Med. Syst. 2020, 44 , 1–12. [Google Scholar] [CrossRef] [PubMed]
66. Gohel, H.A.; Upadhyay, H.; Lagos, L.; Cooper, K.; Sanzetenea, A. Predictive maintenance architecture development for nuclear infrastructure using machine learning. Nucl. Eng. Technol. 2020, 52 , 1436–1442. [Google Scholar] [CrossRef]
67. Li, K.; Zhang, Y.; Song, S.; Zhao, Z.; Wang, L. Study on Life Prediction Method of MOSFET Thermal Environment Experiments Based on Extended Kalman Filter. In Man-Machine-Environment System Engineering, Proceedings of the 20th International Conference on MMESE, Beijing, China, 20–23 October 2020 ; Springer: New Yorik, NY, USA, 2020; pp. 495–503. [Google Scholar]
68. Cheng, J.C.; Chen, W.; Chen, K.; Wang, Q. Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms. Autom. Constr. 2020, 112 , 103087. [Google Scholar] [CrossRef]
69. Kaparthi, S.; Bumblauskas, D. Designing predictive maintenance systems using decision tree-based machine learning techniques. Int. J. Qual. Reliab. Manag. 2020, 37 , 659–686. [Google Scholar] [CrossRef]
70. Hsu, J.-Y.; Wang, Y.-F.; Lin, K.-C.; Chen, M.-Y.; Hsu, J.H.-Y. Wind turbine fault diagnosis and predictive maintenance through statistical process control and machine learning. Ieee Access 2020, 8 , 23427–23439. [Google Scholar] [CrossRef]
71. Bezerra, A.; da Silva, K.C.N.; Nascimento, E. Industrial environment: A strategy for preventive maintenance using Machine Learning to predict the useful life of equipment and Statistical Process Control for Continuous Monitoring of Variables. INFOCOMP J. Comput. Sci. 2023, 22 , 1–9. Available online: https://infocomp.dcc.ufla.br/index.php/infocomp/article/view/3029 (accessed on 19 January 2024).
72. Saidy, C.; Xia, K.; Kircaliali, A.; Harik, R.; Bayoumi, A. The application of statistical quality control methods in predictive maintenance 4.0: An unconventional use of statistical process control (SPC) charts in health monitoring and predictive analytics. In Advances in Asset Management and Condition Monitoring: COMADEM 2019 ; Springer: New York, NY, USA, 2020; pp. 1051–1061. [Google Scholar]
73. Gomes, I.P.; Wolf, D.F. Health monitoring system for autonomous vehicles using dynamic Bayesian networks for diagnosis and prognosis. J. Intell. Robot. Syst. 2021, 101 , 1–21. [Google Scholar] [CrossRef]
74. Devare, M. Predictive Maintenance in Industry 4.0. In Computational Intelligent Security in Wireless Communications ; CRC Press: New York, NY, USA, 2023; pp. 79–98. [Google Scholar]
75. Lakehal, A.; Ramdane, A.; Tachi, F. Probabilistic reasoning for improving the predictive maintenance of vital electrical machine: Case study. J. Adv. Eng. Comput. 2018, 2 , 9–17. [Google Scholar] [CrossRef]
76. Zhang, H.; Marsh, D.W.R. Generic Bayesian network models for making maintenance decisions from available data and expert knowledge. Proc. Inst. Mech. Eng. Part O J. Risk Reliab. 2018, 232 , 505–523. [Google Scholar] [CrossRef]
77. Perini, L. Predictive Maintenance for off-Road Vehicles Based on Hidden Markov Models and Autoencoders for Trend Anomaly Detection. Ph.D. Thesis, Politecnico di Torino, Torino, Italy, 2019. [Google Scholar]
78. Simões, A.; Farinha, J.T.; Fonseca, I. Ecological Predictive Maintenance of Diesel Engines. In Diesel and Gasoline Engines ; IntechOpen: London, UK, 2020; p. 137. [Google Scholar] [CrossRef]
79. Wu, Z.; Luo, H.; Yang, Y.; Lv, P.; Zhu, X.; Ji, Y.; Wu, B. K-PdM: KPI-oriented machinery deterioration estimation framework for predictive maintenance using cluster-based hidden Markov model. IEEE Access 2018, 6 , 41676–41687. [Google Scholar] [CrossRef]
80. Kinghorst, J.; Geramifard, O.; Luo, M.; Chan, H.-L.; Yong, K.; Folmer, J.; Zou, M.; Vogel-Heuser, B. Hidden Markov model-based predictive maintenance in semiconductor manufacturing: A genetic algorithm approach. In Proceedings of the 2017 13th IEEE Conference on Automation Science and Engineering (CASE), Xi’an, China, 20–23 August 2017; pp. 1260–1267. [Google Scholar]
81. Delmas, A.; Sallak, M.; Schön, W.; Zhao, L. Remaining useful life estimation methods for predictive maintenance models: Defining intervals and strategies for incomplete data. In Proceedings of the 10th IMA International Conference on Modelling in Industrial Maintenance and Reliability, Manchester, UK, 13–15 June 2018; pp. 48–53. [Google Scholar]
82. Dutta, N.; Palanisamy, K.; Shanmugam, P.; Subramaniam, U.; Selvam, S. Life Cycle Cost Analysis of Pumping System through Machine Learning and Hidden Markov Model. Processes 2023, 11 , 2157. [Google Scholar] [CrossRef]
83. Gualeni, P.; Vairo, T. A prediction tool for maintenance costs estimation during the design process of a ship engine room. J. Ocean Eng. Mar. Energy 2023, 6 , 653–663. [Google Scholar] [CrossRef]
84. Oliosi, E.; Calzavara, G.; Ferrari, G. On Sensor Data Clustering for Machine Status Monitoring and Its Application to Predictive Maintenance. IEEE Sens. J. 2023, 23 , 9620–9639. [Google Scholar] [CrossRef]
85. Su, C.-J.; Huang, S.-F. Real-time big data analytics for hard disk drive predictive maintenance. Comput. Electr. Eng. 2018, 71 , 93–101. [Google Scholar] [CrossRef]
86. Yu, W.; Dillon, T.; Mostafa, F.; Rahayu, W.; Liu, Y. A global manufacturing big data ecosystem for fault detection in predictive maintenance. IEEE Trans. Ind. Inform. 2019, 16 , 183–192. [Google Scholar] [CrossRef]
87. Kalathas, I.; Papoutsidakis, M. Predictive maintenance using machine learning and data mining: A pioneer method implemented to Greek railways. Designs 2021, 5 , 5. [Google Scholar] [CrossRef]
88. Cao, Q.; Samet, A.; Zanni-Merk, C.; de Bertrand de Beuvron, F.; Reich, C. Combining chronicle mining and semantics for predictive maintenance in manufacturing processes. Semant. Web 2020, 11 , 927–948. [Google Scholar] [CrossRef]
89. Chang, R.-I.; Lee, C.-Y.; Hung, Y.-H. Cloud-based analytics module for predictive maintenance of the textile manufacturing process. Appl. Sci. 2021, 11 , 9945. [Google Scholar] [CrossRef]
90. Proto, S.; Di Corso, E.; Apiletti, D.; Cagliero, L.; Cerquitelli, T.; Malnati, G.; Mazzucchi, D. REDTag: A predictive maintenance framework for parcel delivery services. IEEE Access 2020, 8 , 14953–14964. [Google Scholar] [CrossRef]
91. Bajic, B.; Suzic, N.; Moraca, S.; Stefanović, M.; Jovicic, M.; Rikalovic, A. Edge Computing Data Optimization for Smart Quality Management: Industry 5.0 Perspective. Sustainability 2023, 15 , 6032. [Google Scholar] [CrossRef]
92. Izagirre, U.; Andonegui, I.; Landa-Torres, I.; Zurutuza, U. A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines. Robot. Comput. Integr. Manuf. 2022, 74 , 102287. [Google Scholar] [CrossRef]
93. Balachandar, K.; Jegadeeshwaran, R.; Gandhikumar, D. Condition monitoring of FSW tool using vibration analysis–A machine learning approach. Mater. Today Proc. 2020, 27 , 2970–2974. [Google Scholar] [CrossRef]
94. Pinheiro, A.A.; Brandao, I.M.; Da Costa, C. Vibration analysis in turbomachines using machine learning techniques. Eur. J. Eng. Technol. Res. 2019, 4 , 12–16. [Google Scholar]
95. Bouabdallaoui, Y.; Lafhaj, Z.; Yim, P.; Ducoulombier, L.; Bennadji, B. Predictive maintenance in building facilities: A machine learning-based approach. Sensors 2021, 21 , 1044. [Google Scholar] [CrossRef] [PubMed]
96. Zenisek, J.; Holzinger, F.; Affenzeller, M. Machine learning based concept drift detection for predictive maintenance. Comput. Ind. Eng. 2019, 137 , 106031. [Google Scholar] [CrossRef]
97. King, R.; Curran, K. Predictive Maintenance for Vibration-Related failures in the Semi-Conductor Industry. J. Comput. Eng. Inf. Technol. 2019, 8 , 1. [Google Scholar]
98. Lee, S.; Yu, H.; Yang, H.; Song, I.; Choi, J.; Yang, J.; Lim, G.; Kim, K.-S.; Choi, B.; Kwon, J. A study on deep learning application of vibration data and visualization of defects for predictive maintenance of gravity acceleration equipment. Appl. Sci. 2021, 11 , 1564. [Google Scholar] [CrossRef]
99. Haggag, S. Vibration analysis for predictive maintenance and improved reliability of rotating machines in ETRR-2 research reactor. Kerntechnik 2022, 87 , 125–134. [Google Scholar] [CrossRef]
100. Karakose, M.; Yaman, O. Complex fuzzy system based predictive maintenance approach in railways. IEEE Trans. Ind. Inform. 2020, 16 , 6023–6032. [Google Scholar] [CrossRef]
101. Pathirathna, K.A.B.; Dhanushka, R.M.; Rathnayake, M.; Hathanguruge, W.; Fernando, G.D. Use of thermal imaging technology for locomotive maintenance in Sri Lanka Railways. In Proceedings of the 2018 International Conference on Intelligent Rail Transportation (ICIRT), Singapore, 12–14 December 2018; pp. 1–4. [Google Scholar]
102. Andritoi, D.; Luca, C.; Corciova, C.; Ciorap, R. The use of thermography as a prediction element in the maintenance of medical equipment. In Proceedings of the 6th International Conference on Advancements of Medicine and Health Care through Technology, Cluj-Napoca, Romania, 17–20 October 2018; pp. 73–78. [Google Scholar]
103. Marinescu, A.-D.; Chiriţă, A.-P.; Cristescu, C.; Safta, C.-A. Numerical Simulation of Thermal Processes Occurring at Testing Hydrostatic Pumps in Cavitation Mode. Hidraulica 2018, 1 , 55–64. [Google Scholar]
104. Arena, S.; Florian, E.; Zennaro, I.; Orrù, P.F.; Sgarbossa, F. A novel decision support system for managing predictive maintenance strategies based on machine learning approaches. Saf. Sci. 2022, 146 , 105529. [Google Scholar] [CrossRef]
105. Kostoláni, M.; Murín, J.; Kozák, Š. Intelligent predictive maintenance control using augmented reality. In Proceedings of the 2019 22nd International Conference on Process Control (PC19), Strbske Pleso, Slovakia, 11–14 June 2019; pp. 131–135. [Google Scholar]
106. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. Intelligent predictive maintenance and remote monitoring framework for industrial equipment based on mixed reality. Front. Mech. Eng. 2020, 6 , 578379. [Google Scholar] [CrossRef]
107. Liu, C.; Zhu, H.; Tang, D.; Nie, Q.; Zhou, T.; Wang, L.; Song, Y. Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing. Robot. Comput. Integr. Manuf. 2022, 77 , 102357. [Google Scholar] [CrossRef]
108. Deshpande, A.; Buktar, R. Convergence of Augmented Reality, Internet of Things (Iot) and Machine Learning for Visualizing Finite Element Analysis Results and Predictive Maintenance. 2023. Available online: https://ssrn.com/abstract=4559683 (accessed on 19 January 2024). [CrossRef]
109. Wellsandt, S.; Klein, K.; Hribernik, K.; Lewandowski, M.; Bousdekis, A.; Mentzas, G.; Thoben, K.-D. Hybrid-augmented intelligence in predictive maintenance with digital intelligent assistants. Annu. Rev. Control 2022, 53 , 382–390. [Google Scholar] [CrossRef]
110. Ansari, F.; Glawar, R.; Nemeth, T. PriMa: A prescriptive maintenance model for cyber-physical production systems. Int. J. Comput. Integr. Manuf. 2019, 32 , 482–503. [Google Scholar] [CrossRef]
111. Gordon, C.A.K.; Burnak, B.; Onel, M.; Pistikopoulos, E.N. Data-driven prescriptive maintenance: Failure prediction using ensemble support vector classification for optimal process and maintenance scheduling. Ind. Eng. Chem. Res. 2020, 59 , 19607–19622. [Google Scholar] [CrossRef]
112. Elbasheer, M.; Longo, F.; Mirabelli, G.; Padovano, A.; Solina, V.; Talarico, S. Integrated Prescriptive Maintenance and Production Planning: A Machine Learning Approach for the Development of an Autonomous Decision Support Agent. IFAC-PapersOnLine 2022, 55 , 2605–2610. [Google Scholar] [CrossRef]
113. Goby, N.; Brandt, T.; Neumann, D. Deep reinforcement learning with combinatorial actions spaces: An application to prescriptive maintenance. Comput. Ind. Eng. 2023, 179 , 109165. [Google Scholar] [CrossRef]
114. Liu, B.; Lin, J.; Zhang, L.; Kumar, U. A dynamic prescriptive maintenance model considering system aging and degradation. IEEE Access 2019, 7 , 94931–94943. [Google Scholar] [CrossRef]
115. Meissner, R.; Rahn, A.; Wicke, K. Developing prescriptive maintenance strategies in the aviation industry based on a discrete-event simulation framework for post-prognostics decision making. Reliab. Eng. Syst. Saf. 2021, 214 , 107812. [Google Scholar] [CrossRef]
116. Yu, W.; Liu, Y.; Dillon, T.; Rahayu, W. Edge computing-assisted IoT framework with an autoencoder for fault detection in manufacturing predictive maintenance. IEEE Trans. Ind. Inform. 2022, 19 , 5701–5710. [Google Scholar] [CrossRef]
117. Cheng, C.; Zhang, B.-K.; Gao, D. A predictive maintenance solution for bearing production line based on edge-cloud cooperation. In Proceedings of the 2019 Chinese Automation Congress (CAC), Hangzhou, China, 22–24 November 2019; pp. 5885–5889. [Google Scholar]
118. Bowden, D.; Marguglio, A.; Morabito, L.; Napione, C.; Panicucci, S.; Nikolakis, N.; Makris, S.; Coppo, G.; Andolina, S.; Macii, A. A Cloud-to-edge Architecture for Predictive Analytics. In Proceedings of the EDBT/ICDT Workshops, Lisbon, Portugal, 26 March 2019. [Google Scholar]
119. Zhang, W.; Lu, Q.; Yu, Q.; Li, Z.; Liu, Y.; Lo, S.K.; Chen, S.; Xu, X.; Zhu, L. Blockchain-based federated learning for device failure detection in industrial IoT. IEEE Internet Things J. 2020, 8 , 5926–5937. [Google Scholar] [CrossRef]
120. Oladapo, K.A.; Adedeji, F.; Nzenwata, U.J.; Quoc, B.P.; Dada, A. Fuzzified Case-Based Reasoning Blockchain Framework for Predictive Maintenance in Industry 4.0. In Data Analytics and Computational Intelligence: Novel Models, Algorithms and Applications ; Springer: New York, NY, USA, 2023; pp. 269–297. [Google Scholar]
121. Kaul, K.; Singh, P.; Jain, D.; Johri, P.; Pandey, A.K. Monitoring and Controlling of Energy Consumption using IOT-based Predictive Maintenance. In Proceedings of the 2021 10th International Conference on System Modeling & Advancement in Research Trends (SMART), Moradabad, India, 10–11 December 2021; pp. 587–594. [Google Scholar]
122. Holmer, O.; Frisk, E.; Krysander, M. Energy-Based Survival Models for Predictive Maintenance. arXiv 2023, arXiv:2302.00629. [Google Scholar] [CrossRef]
123. Orošnjak, M.; Brkljač, N.; Šević, D.; Čavić, M.; Oros, D.; Penčić, M. From predictive to energy-based maintenance paradigm: Achieving cleaner production through functional-productiveness. J. Clean. Prod. 2023, 408 , 137177. [Google Scholar] [CrossRef]
124. Anagiannis, I.; Nikolakis, N.; Alexopoulos, K. Energy-based prognosis of the remaining useful life of the coating segments in hot rolling mill. Appl. Sci. 2020, 10 , 6827. [Google Scholar] [CrossRef]
125. Elahi, M.; Afolaranmi, S.O.; Mohammed, W.M.; Martinez Lastra, J.L. Energy-based prognostics for gradual loss of conveyor belt tension in discrete manufacturing systems. Energies 2022, 15 , 4705. [Google Scholar] [CrossRef]
126. Fila, R.; El Khaili, M.; Mestari, M. Cloud computing for industrial predictive maintenance based on prognostics and health management. Procedia Comput. Sci. 2020, 177 , 631–638. [Google Scholar] [CrossRef]
127. Calabrese, F.; Regattieri, A.; Bortolini, M.; Gamberi, M.; Pilati, F. Predictive maintenance: A novel framework for a data-driven, semi-supervised, and partially online prognostic health management application in industries. Appl. Sci. 2021, 11 , 3380. [Google Scholar] [CrossRef]
128. Saidy, C.; Valappil, S.P.; Matthews, R.M.; Bayoumi, A. Development of a predictive maintenance 4.0 platform: Enhancing product design and manufacturing. In Advances in Asset Management and Condition Monitoring: COMADEM 2019 ; Springer: New York, NY, USA, 2020; pp. 1039–1049. [Google Scholar]
129. Razali, M.N.; Jamaluddin, A.F.; Abdul Jalil, R.; Nguyen, T.K. Big data analytics for predictive maintenance in maintenance management. Prop. Manag. 2020, 38 , 513–529. [Google Scholar] [CrossRef]
130. De Leon, V.; Alcazar, Y.; Villa, J.L. Use of edge computing for predictive maintenance of industrial electric motors. In Proceedings of the Applied Computer Sciences in Engineering: 6th Workshop on Engineering Applications, WEA 2019, Proceedings 6, Santa Marta, Colombia, 16–18 October 2019; pp. 523–533. [Google Scholar]
131. Yin, J.; Luo, X.; Zhu, Y.; Wang, W.; Wang, L.; Huang, C.; Wang, J.H. An edge computing-based predictive evaluation scheme toward geological drilling data using long short-term memory network. Trans. Emerg. Telecommun. Technol. 2021, 32 , e3888. [Google Scholar] [CrossRef]
132. Saxena, A.; Goebel, K. Turbofan Engine Degradation Simulation Data Set. NASA Ames Progn. Data Repos. 2008. Available online: https://data.nasa.gov/Aerospace/CMAPSS-Jet-Engine-Simulated-Data/ff5v-kuh6/ (accessed on 19 January 2024).
133. Wen, M.-S.; Wang, C.-Y.; Yeh, J.-K.; Chen, C.-C.; Tsai, M.-L.; Ho, M.-Y.; Hung, K.-C.; Hsieh, I. The role of Asprosin in patients with dilated cardiomyopathy. BMC Cardiovasc. Disord. 2020, 20 , 1–8. [Google Scholar] [CrossRef] [PubMed]
134. Saxena, A.; Goebel, K. Phm08 challenge data set. In NASA Ames Prognostics Data Repository ; NASA Ames Research Center: Moffett Field, CA, USA, (consulted 2014-02-15); 2008. Available online: http://ti.arc.nasa.gov/project/prognostic-data-repository (accessed on 19 January 2024).
135. Agogino, A.; Goebel, K. Milling data set. In NASA Ames Prognostics Data Repository ; NASA Ames Research Center: Moffett Field, CA, USA, 2007. Available online: http://ti.arc.nasa.gov/project/prognostic-data-repository (accessed on 19 January 2024).
136. Lee, J.; Qiu, H.; Yu, G.; Lin, J. Bearing Data Set. IMS, University of Cincinnati, NASA Ames Prognostics Data Repository, Rexnord Technical Services. 2007. Available online: https://www.nasa.gov/intelligent-systems-division/discovery-and-systems-health/pcoe/pcoe-data-set-repository/ (accessed on 19 January 2024).
137. Bearing Data Center. Case Western Reserve University Bearing Data ; Bearing Data Center: Cleveland, OH, USA, 2013. [Google Scholar]
138. FEMTO Bearing Data Set ; FEMTO-ST Institute: Besançon, France, 2012; IEEE PHM 2012 Data Challenge; Available online: https://www.femto-st.fr/en/Research-departments/AS2M/Research-groups/DATA-PHM (accessed on 19 January 2024).
139. Nectoux, P.; Gouriveau, R.; Medjaher, K.; Ramasso, E.; Chebel-Morello, B.; Zerhouni, N.; Varnier, C. PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In Proceedings of the IEEE International Conference on Prognostics and Health Management, PHM’12, Minneapolis, MN, USA, 23–27 September 2012; pp. 1–8. [Google Scholar]
140. Backblaze. Hard Drive Data and Stats 2019. Available online: https://www.backblaze.com/b2/hard-drive-test-data.html (accessed on 30 December 2023).
141. PAKDD2020 Alibaba AI OPS Competition. 2020. Available online: https://tianchi.aliyun.com/competition/entrance/231775/introduction (accessed on 30 December 2023).
142. Saha, B.; Goebel, K. Battery data set. In NASA Ames Prognostics Data Repository ; NASA Ames Research Center: Moffett Field, CA, USA, 2007. Available online: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ (accessed on 19 January 2024).
143. Hasib, S.A.; Islam, S.; Chakrabortty, R.K.; Ryan, M.J.; Saha, D.K.; Ahamed, M.H.; Moyeen, S.I.; Das, S.K.; Ali, M.F.; Islam, M.R. A comprehensive review of available battery datasets, RUL prediction approaches, and advanced battery management. IEEE Access 2021, 9 , 86166–86193. [Google Scholar] [CrossRef]
144. Celaya, J.; Saxena, A.; Saha, S.; Goebel, K. MOSFET thermal overstress aging data set. In NASA Ames Prognostics Data Repository ; NASA Ames Research Center: Moffett Field, CA, USA, 2011. Available online: https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/ (accessed on 19 January 2024).
145. Ribeiro, F. MaFaulDa-Machinery Fault Database ; Signals, Multimedia, and Telecommunications Laboratory: Rio de Janeiro, Brazil, 2016. [Google Scholar]
146. Microsoft Azure. Azure ai Guide for Predictive Maintenance Solutions. 2020. Available online: https://docs.microsoft.com/pt-br/azure/machine-learning/team-data-science-process/predictive-maintenance-playbook#solution-templates-for-predictive-maintenance (accessed on 30 December 2023).
147. Hong, T.; Pinson, P.; Fan, S.; Zareipour, H.; Troccoli, A.; Hyndman, R.J. Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond. Int. J. Forecast. 2016, 32 , 896–913. [Google Scholar] [CrossRef]
148. McCann, M.; Johnston, A. SECOM Dataset UCI Machine Learning Repository. 2008. Available online: https://archive.ics.uci.edu/ml/datasets/secom (accessed on 19 January 2024).
149. UN Statistics Division. International Standard Industrial Classification of All Economic Activities (ISIC) ; United Nations Publications: New York, NY, USA, 2008. [Google Scholar]
150. Arrieta, A.B.; Díaz-Rodríguez, N.; Del Ser, J.; Bennetot, A.; Tabik, S.; Barbado, A.; García, S.; Gil-López, S.; Molina, D.; Benjamins, R. Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI. Inf. Fusion 2020, 58 , 82–115. [Google Scholar] [CrossRef]
151. Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 2019, 1 , 206–215. [Google Scholar] [CrossRef]
152. Gilpin, L.H.; Bau, D.; Yuan, B.Z.; Bajwa, A.; Specter, M.; Kagal, L. Explaining explanations: An overview of interpretability of machine learning. In Proceedings of the 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA), Turin, Italy, 1–3 October 2018; pp. 80–89. [Google Scholar]
153. Fan, F.-L.; Xiong, J.; Li, M.; Wang, G. On interpretability of artificial neural networks: A survey. IEEE Trans. Radiat. Plasma Med. Sci. 2021, 5 , 741–760. [Google Scholar] [CrossRef]
154. Slack, D.; Hilgard, S.; Jia, E.; Singh, S.; Lakkaraju, H. Fooling lime and shap: Adversarial attacks on post hoc explanation methods. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, New York, NY, USA, 7–8 February 2020; pp. 180–186. [Google Scholar]
155. Ribeiro, M.T.; Singh, S.; Guestrin, C. “Why should i trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 13–17 August 2016; pp. 1135–1144. [Google Scholar]
156. Lundberg, S.M.; Erion, G.; Chen, H.; DeGrave, A.; Prutkin, J.M.; Nair, B.; Katz, R.; Himmelfarb, J.; Bansal, N.; Lee, S.-I. From local explanations to global understanding with explainable AI for trees. Nat. Mach. Intell. 2020, 2 , 56–67. [Google Scholar] [CrossRef]
157. Zhou, B.; Khosla, A.; Lapedriza, A.; Oliva, A.; Torralba, A. Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 27–30 June 2016; pp. 2921–2929. [Google Scholar]
158. Bach, S.; Binder, A.; Montavon, G.; Klauschen, F.; Müller, K.-R.; Samek, W. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 2015, 10 , e0130140. [Google Scholar] [CrossRef]
159. Selvaraju, R.R.; Cogswell, M.; Das, A.; Vedantam, R.; Parikh, D.; Batra, D. Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 27–29 October 2017; pp. 618–626. [Google Scholar]
160. Nor, A.K.M.; Pedapati, S.R.; Muhammad, M.; Leiva, V. Overview of explainable artificial intelligence for prognostic and health management of industrial assets based on preferred reporting items for systematic reviews and meta-analyses. Sensors 2021, 21 , 8020. [Google Scholar] [CrossRef]
161. Došilović, F.K.; Brčić, M.; Hlupić, N. Explainable artificial intelligence: A survey. In Proceedings of the 2018 41st International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia, 21–25 May 2018; pp. 0210–0215. [Google Scholar]
162. Carvalho, D.V.; Pereira, E.M.; Cardoso, J.S. Machine learning interpretability: A survey on methods and metrics. Electronics 2019, 8 , 832. [Google Scholar] [CrossRef]
163. Molnar, C. Interpretable Machine Learning. 2020. Available online: https://christophm.github.io/interpretable-ml-book/ (accessed on 19 January 2024).
164. Izza, Y.; Ignatiev, A.; Marques-Silva, J. On explaining decision trees. arXiv 2020, arXiv:2010.11034. [Google Scholar]
165. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, Ł.; Polosukhin, I. Attention is all you need. Adv. Neural Inf. Process. Syst. 2017, 30 , 6000–6010. [Google Scholar]
166. Alhassan, A.M.; Zainon, W.M.N.W. Review of feature selection, dimensionality reduction and classification for chronic disease diagnosis. IEEE Access 2021, 9 , 87310–87317. [Google Scholar] [CrossRef]
167. Blum, A.L.; Langley, P. Selection of relevant features and examples in machine learning. Artif. Intell. 1997, 97 , 245–271. [Google Scholar] [CrossRef]
168. Ferreira, A.J.; Figueiredo, M.A. Efficient feature selection filters for high-dimensional data. Pattern Recognit. Lett. 2012, 33 , 1794–1804. [Google Scholar] [CrossRef]
169. Cernuda, C. On the relevance of preprocessing in predictive maintenance for dynamic systems. In Predictive Maintenance in Dynamic Systems: Advanced Methods, Decision Support Tools and Real-World Applications ; Springer: New York, NY, USA, 2019; pp. 53–93. [Google Scholar]
170. Ojala, J. On Analysis of the Predictive Maintenance of Railway Points Processes and Possibilities. Master’s Thesis, School of Science, Aalto University, Espoo, Finland, 14 May 2018. [Google Scholar]
171. Mahalle, P.N.; Hujare, P.P.; Shinde, G.R. Predictive Analytics for Mechanical Engineering: A Beginners Guide ; Springer Nature: New York, NY, USA, 2023. [Google Scholar]
172. Baaj, I. Explainability of Possibilistic and Fuzzy Rule-Based Systems. Ph.D. Thesis, Sorbonne Université, Paris, France, 2022. [Google Scholar]
173. Duan, H.; Ökten, G. Derivative-based Shapley value for global sensitivity analysis and machine learning explainability. arXiv 2023, arXiv:2303.15183. [Google Scholar]
174. Hermansa, M.; Kozielski, M.; Michalak, M.; Szczyrba, K.; Wróbel, Ł.; Sikora, M. Sensor-based predictive maintenance with reduction of false alarms—A case study in heavy industry. Sensors 2021, 22 , 226. [Google Scholar] [CrossRef]
175. Kozielski, M. Contextual Explanations for Decision Support in Predictive Maintenance. Appl. Sci. 2023, 13 , 10068. [Google Scholar] [CrossRef]
176. Kuzlu, M.; Cali, U.; Sharma, V.; Güler, Ö. Gaining insight into solar photovoltaic power generation forecasting utilizing explainable artificial intelligence tools. IEEE Access 2020, 8 , 187814–187823. [Google Scholar] [CrossRef]
177. Serradilla, O.; Zugasti, E.; Ramirez de Okariz, J.; Rodriguez, J.; Zurutuza, U. Adaptable and explainable predictive maintenance: Semi-supervised deep learning for anomaly detection and diagnosis in press machine data. Appl. Sci. 2021, 11 , 7376. [Google Scholar] [CrossRef]
178. Ferraro, A.; Galli, A.; Moscato, V.; Sperlì, G. Evaluating explainable artificial intelligence tools for hard disk drive predictive maintenance. Artif. Intell. Rev. 2023, 56 , 7279–7314. [Google Scholar] [CrossRef]
179. Youness, G.; Aalah, A. An Explainable Artificial Intelligence Approach for Remaining Useful Life Prediction. Aerospace 2023, 10 , 474. [Google Scholar] [CrossRef]
180. Hajgató, G.; Wéber, R.; Szilágyi, B.; Tóthpál, B.; Gyires-Tóth, B.; Hős, C. PredMaX: Predictive maintenance with explainable deep convolutional autoencoders. Adv. Eng. Inform. 2022, 54 , 101778. [Google Scholar] [CrossRef]
181. Hong, C.W.; Lee, C.; Lee, K.; Ko, M.-S.; Kim, D.E.; Hur, K. Remaining useful life prognosis for turbofan engine using explainable deep neural networks with dimensionality reduction. Sensors 2020, 20 , 6626. [Google Scholar] [CrossRef] [PubMed]
182. Usuga-Cadavid, J.P.; Lamouri, S.; Grabot, B.; Fortin, A. Using deep learning to value free-form text data for predictive maintenance. Int. J. Prod. Res. 2022, 60 , 4548–4575. [Google Scholar] [CrossRef]
183. Wu, H.; Huang, A.; Sutherland, J.W. Layer-wise relevance propagation for interpreting LSTM-RNN decisions in predictive maintenance. Int. J. Adv. Manuf. Technol. 2022, 118 , 963–978. [Google Scholar] [CrossRef]
184. Cao, Q.; Zanni-Merk, C.; Samet, A.; Reich, C.; De Beuvron, F.D.B.; Beckmann, A.; Giannetti, C. KSPMI: A knowledge-based system for predictive maintenance in industry 4.0. Robot. Comput. Integr. Manuf. 2022, 74 , 102281. [Google Scholar] [CrossRef]
185. Chen, W. A Rule-Based Expert System for Predictive Maintenance of a Hybrid Bus. Ph.D. Thesis, Université d’Ottawa/University of Ottawa, Ottawa, ON, Canada, 2020. [Google Scholar]
186. Upasane, S.J.; Hagras, H.; Anisi, M.H.; Savill, S.; Taylor, I.; Manousakis, K. A Type-2 Fuzzy Based Explainable AI System for Predictive Maintenance within the Water Pumping Industry. In IEEE Transactions on Artificial Intelligence ; IEEE: New York, NY, USA, 2023. [Google Scholar]
187. De Bernardi, G.; Narteni, S.; Cambiaso, E.; Mongelli, M. Rule-based out-of-distribution detection. arXiv 2023, arXiv:2303.01860. [Google Scholar] [CrossRef]
188. Christou, I.T.; Kefalakis, N.; Zalonis, A.; Soldatos, J. Predictive and explainable machine learning for industrial internet of things applications. In Proceedings of the 2020 16th International Conference on Distributed Computing in Sensor Systems (DCOSS), Marina del Rey, CA, USA, 25–27 May 2020; pp. 213–218. [Google Scholar]
189. Betz, W.; Papaioannou, I.; Zeh, T.; Hesping, D.; Krauss, T.; Straub, D. Data-Driven Predictive Maintenance for Gas Distribution Networks. ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A Civ. Eng. 2022, 8 , 04022016. [Google Scholar] [CrossRef]
190. Matzka, S. Explainable artificial intelligence for predictive maintenance applications. In Proceedings of the 2020 Third International Conference on Artificial Intelligence for Industries (ai4i), Irvine, CA, USA, 21–23 September 2020; pp. 69–74. [Google Scholar]
191. Hrnjica, B.; Softic, S. Explainable AI in manufacturing: A predictive maintenance case study. In Proceedings of the IFIP International Conference on Advances in Production Management Systems, Novi Sad, Serbia, 30 August–3 September 2020; pp. 66–73. [Google Scholar]
192. Garouani, M.; Ahmad, A.; Bouneffa, M.; Hamlich, M.; Bourguin, G.; Lewandowski, A. Towards big industrial data mining through explainable automated machine learning. Int. J. Adv. Manuf. Technol. 2022, 120 , 1169–1188. [Google Scholar] [CrossRef]
193. Yan, W.; Shi, Y.; Ji, Z.; Sui, Y.; Tian, Z.; Wang, W.; Cao, Q. Intelligent predictive maintenance of hydraulic systems based on virtual knowledge graph. Eng. Appl. Artif. Intell. 2023, 126 , 106798. [Google Scholar] [CrossRef]
194. Liu, C.; Tang, D.; Zhu, H.; Nie, Q. A novel predictive maintenance method based on deep adversarial learning in the intelligent manufacturing system. IEEE Access 2021, 9 , 49557–49575. [Google Scholar] [CrossRef]
195. Shah, S.R.B.; Chadha, G.S.; Schwung, A.; Ding, S.X. A sequence-to-sequence approach for remaining useful lifetime estimation using attention-augmented bidirectional lstm. Intell. Syst. Appl. 2021, 10 , 200049. [Google Scholar] [CrossRef]
196. Li, J.; Li, S.; Ding, Z.; Zheng, A.; Ye, X. Bidirectional self-Attention Gated Recurrent Unit for Health Index Prediction of Rolling Bearings. In Proceedings of the 2023 42nd Chinese Control Conference (CCC), Tianjin, China, 24–26 July 2023; pp. 6656–6662. [Google Scholar]
197. Behizadi, H.R. Application of Attention Mechanism in Deep Neural Network Architecture for System Failure Prognostics. Ph.D. Thesis, Concordia University, Montreal, QC, Canada, 2023. [Google Scholar]
198. Yu, Y.; Chen, H. Synergistic Signal Denoising for Multimodal Time Series of Structure Vibration. arXiv 2023, arXiv:2308.11644. [Google Scholar]
199. Xia, S.; Zhou, X.; Shi, H.; Li, S.; Xu, C. A fault diagnosis method with multi-source data fusion based on hierarchical attention for AUV. Ocean Eng. 2022, 266 , 112595. [Google Scholar] [CrossRef]
200. De Luca, R.; Ferraro, A.; Galli, A.; Gallo, M.; Moscato, V.; Sperli, G. A deep attention based approach for predictive maintenance applications in IoT scenarios. J. Manuf. Technol. Manag. 2023, 34 , 535–556. [Google Scholar] [CrossRef]
201. Barraza, J.F.; Droguett, E.L.; Martins, M.R. FS-SCF network: Neural network interpretability based on counterfactual generation and feature selection for fault diagnosis. Expert Syst. Appl. 2024, 237 , 121670. [Google Scholar] [CrossRef]
202. Χριστοδούλου, Γ.A. Interpretable Predictive Maintenance: Global and Local Dimensionality Reduction Approach. Ph.D. Thesis, Aristotle University of Thessaloniki, Thessaloniki, Greece, 2022. [Google Scholar]
203. Alfeo, A.L.; Cimino, M.G.; Vaglini, G. Degradation stage classification via interpretable feature learning. J. Manuf. Syst. 2022, 62 , 972–983. [Google Scholar] [CrossRef]
204. Giordano, D.; Giobergia, F.; Pastor, E.; La Macchia, A.; Cerquitelli, T.; Baralis, E.; Mellia, M.; Tricarico, D. Data-driven strategies for predictive maintenance: Lesson learned from an automotive use case. Comput. Ind. 2022, 134 , 103554. [Google Scholar] [CrossRef]
205. Jing, T.; Zheng, P.; Xia, L.; Liu, T. Transformer-based hierarchical latent space VAE for interpretable remaining useful life prediction. Adv. Eng. Inform. 2022, 54 , 101781. [Google Scholar] [CrossRef]
206. Mazzocca, C.; Romandini, N.; Mendula, M.; Montanari, R.; Bellavista, P. TruFLaaS: Trustworthy Federated Learning as a Service. IEEE Internet Things J. 2023, 10 , 21266–21281. [Google Scholar] [CrossRef]
207. Ghasemkhani, B.; Aktas, O.; Birant, D. Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing. Machines 2023, 11 , 322. [Google Scholar] [CrossRef]
208. Abbas, A.N.; Chasparis, G.; Kelleher, J.D. Interpretable Hidden Markov Model-Based Deep Reinforcement Learning Hierarchical Framework for Predictive Maintenance of Turbofan Engines. arXiv 2022, arXiv:2206.13433. [Google Scholar]
209. Gilles, O.; Pérez, D.G.; Brameret, P.-A.; Lacroix, V. Securing IIot communications using OPC UA pubsub and trusted platform modules. J. Syst. Archit. 2023, 134 , 102797. [Google Scholar] [CrossRef]
210. Unal, P.; Albayrak, Ö.; Jomâa, M.; Berre, A.J. Data-driven artificial intelligence and predictive analytics for the maintenance of industrial machinery with hybrid and cognitive digital twins. In Technologies and Applications for Big Data Value ; Springer: New York, NY, USA, 2022; pp. 299–319. [Google Scholar]
211. Barig, B.; Balzereit, K.; Hutschenreuther, T. Applying OPC-UA for factory-wide industrial assistance systems. In Proceedings of the 2019 15th IEEE International Workshop on Factory Communication Systems (WFCS), Sundsvall, Sweden, 27–29 May 2019; pp. 1–4. [Google Scholar]
212. Kedari, S.; Kulkarni, S.; Vishwakarma, C.; Korgaonkar, J.; Warke, N. Remote monitoring and predictive maintenance in bottle filling process using industry 4.0 concepts. In AIP Conference Proceedings ; AIP Publishing: New York, NY, USA, 2022. [Google Scholar]
213. Dosluoglu, T.; MacDonald, M. Circuit Design for Predictive Maintenance. arXiv 2022, arXiv:2211.10248. [Google Scholar] [CrossRef]
214. Balla, M.; Haffner, O.; Kučera, E.; Cigánek, J. Educational Case Studies: Creating a Digital Twin of the Production Line in TIA Portal, Unity, and Game4Automation Framework. Sensors 2023, 23 , 4977. [Google Scholar] [CrossRef] [PubMed]
215. Ryalat, M.; ElMoaqet, H.; AlFaouri, M. Design of a smart factory based on cyber-physical systems and Internet of Things towards Industry 4.0. Appl. Sci. 2023, 13 , 2156. [Google Scholar] [CrossRef]
216. Killeen, P. Knowledge-Based Predictive Maintenance for Fleet Management. Ph.D. Thesis, Université d’Ottawa/University of Ottawa, Ottawa, ON, Canada, 2020. [Google Scholar]
217. Shin, S.-J. An opc ua-compliant interface of data analytics models for interoperable manufacturing intelligence. IEEE Trans. Ind. Inform. 2020, 17 , 3588–3598. [Google Scholar] [CrossRef]
218. Bojarczuk, G.; Mazur, M.; Wojciechowski, A.; Olszewski, M. Artificial Intelligence in Predicting Abnormal States in a Robotic Production Stand. Pomiary Autom. Robot. 2021, 25 , 5–22. [Google Scholar] [CrossRef]
219. Allahloh, A.S.; Sarfraz, M.; Ghaleb, A.M.; Al-Shamma’a, A.A.; Hussein Farh, H.M.; Al-Shaalan, A.M. Revolutionizing IC Genset Operations with IIoT and AI: A Study on Fuel Savings and Predictive Maintenance. Sustainability 2023, 15 , 8808. [Google Scholar] [CrossRef]
220. Mourtzis, D.; Angelopoulos, J.; Panopoulos, N. Design and Development of an Edge-Computing Platform Towards 5G Technology Adoption for Improving Equipment Predictive Maintenance. Procedia Comput. Sci. 2022, 200 , 611–619. [Google Scholar] [CrossRef]
221. Hafeez, T.; Xu, L.; Mcardle, G. Edge intelligence for data handling and predictive maintenance in IIOT. IEEE Access 2021, 9 , 49355–49371. [Google Scholar] [CrossRef]
222. Foukalas, F. Cognitive IoT platform for fog computing industrial applications. Comput. Electr. Eng. 2020, 87 , 106770. [Google Scholar] [CrossRef]
223. Fernandes, M.; Canito, A.; Mota, D.; Corchado, J.M.; Marreiros, G. Service-oriented architecture for data-driven fault detection. In Proceedings of the International Symposium on Distributed Computing and Artificial Intelligence, Salamanca, Spain, 14 May 2021; pp. 179–189. [Google Scholar]
224. Bulut, B.; Ketmen, H.B.; Atalay, A.S.; Herkiloğlu, O.; Salokangas, R. An Arrowhead and Mimosa Based IoT Framework with an Industrial Predictive Maintenance Application. In Proceedings of the 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Kocaeli, Turkey, 25–27 August 2021; pp. 1–5. [Google Scholar]
225. Greci, L. XR for Industrial Training & Maintenance. In Roadmapping Extended Reality: Fundamentals and Applications ; John Wiley & Sons: New York, NY, USA, 2022; pp. 309–320. [Google Scholar]
226. Pujana, A.; Esteras, M.; Perea, E.; Maqueda, E.; Calvez, P. Hybrid-Model-Based Digital Twin of the Drivetrain of a Wind Turbine and Its Application for Failure Synthetic Data Generation. Energies 2023, 16 , 861. [Google Scholar] [CrossRef]
227. Stamoulis, K. Innovations in the Aviation MRO: Adaptive, Digital, and Sustainable Tools for Smarter Engineering and Maintenance ; Eburon Academic Publishers: Delft, The Netherlands, 2022. [Google Scholar]
228. Rossini, R.; Prato, G.; Conzon, D.; Pastrone, C.; Pereira, E.; Reis, J.; Gonçalves, G.; Henriques, D.; Santiago, A.R.; Ferreira, A. AI environment for predictive maintenance in a manufacturing scenario. In Proceedings of the 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vasteras, Sweden, 7–10 September 2021; pp. 1–8. [Google Scholar]
229. Klein, P.; Bergmann, R. Generation of Complex Data for AI-based Predictive Maintenance Research with a Physical Factory Model. In Proceedings of the ICINCO (1), Prague, Czech Republic, 29–31 July 2019; pp. 40–50. [Google Scholar]
230. Wolfartsberger, J.; Zenisek, J.; Wild, N. Data-driven maintenance: Combining predictive maintenance and mixed reality-supported remote assistance. Procedia Manuf. 2020, 45 , 307–312. [Google Scholar] [CrossRef]
231. Bekar, E.T.; Nyqvist, P.; Skoogh, A. An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study. Adv. Mech. Eng. 2020, 12 , 1687814020919207. [Google Scholar] [CrossRef]
232. Kamariotis, A.; Tatsis, K.; Chatzi, E.; Goebel, K.; Straub, D. A metric for assessing and optimizing data-driven prognostic algorithms for predictive maintenance. Reliab. Eng. Syst. Saf. 2024, 242 , 109723. [Google Scholar] [CrossRef]
233. Kong, Q.; Lu, R.; Yin, F.; Cui, S. Privacy-preserving continuous data collection for predictive maintenance in vehicular fog-cloud. IEEE Trans. Intell. Transp. Syst. 2020, 22 , 5060–5070. [Google Scholar] [CrossRef]
234. Liu, Y.; Yu, W.; Dillon, T.; Rahayu, W.; Li, M. Empowering IoT predictive maintenance solutions with AI: A distributed system for manufacturing plant-wide monitoring. IEEE Trans. Ind. Inform. 2021, 18 , 1345–1354. [Google Scholar] [CrossRef]
235. von Enzberg, S.; Naskos, A.; Metaxa, I.; Köchling, D.; Kühn, A. Implementation and transfer of predictive analytics for smart maintenance: A case study. Front. Comput. Sci. 2020, 2 , 578469. [Google Scholar] [CrossRef]
236. Bibri, S.E.; Jagatheesaperumal, S.K. Harnessing the potential of the metaverse and artificial intelligence for the internet of city things: Cost-effective XReality and synergistic AIoT technologies. Smart Cities 2023, 6 , 2397–2429. [Google Scholar] [CrossRef]
237. Ouyang, L.; Wu, J.; Jiang, X.; Almeida, D.; Wainwright, C.; Mishkin, P.; Zhang, C.; Agarwal, S.; Slama, K.; Ray, A. Training language models to follow instructions with human feedback. Adv. Neural Inf. Process. Syst. 2022, 35 , 27730–27744. [Google Scholar]
238. Bubeck, S.; Chandrasekaran, V.; Eldan, R.; Gehrke, J.; Horvitz, E.; Kamar, E.; Lee, P.; Lee, Y.T.; Li, Y.; Lundberg, S. Sparks of artificial general intelligence: Early experiments with gpt-4. arXiv 2023, arXiv:2303.12712. [Google Scholar]
239. Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H.W.; Sutton, C.; Gehrmann, S. Palm: Scaling language modeling with pathways. arXiv 2022, arXiv:2204.02311. [Google Scholar]
240. Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.-A.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F. Llama: Open and efficient foundation language models. arXiv 2023, arXiv:2302.13971. [Google Scholar]
241. Rane, N. Potential Role and Challenges of ChatGPT and Similar Generative Artificial Intelligence in Architectural Engineering. 2023. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4607767 (accessed on 19 January 2024).
242. Rane, N. Role of ChatGPT and Similar Generative Artificial Intelligence (AI) in Construction Industry. 2023. Available online: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4598258 (accessed on 19 January 2024).
243. Ghimire, P.; Kim, K.; Acharya, M. Generative ai in the construction industry: Opportunities & challenges. arXiv 2023, arXiv:2310.04427. [Google Scholar]
244. Rane, N.L. Multidisciplinary Collaboration: Key Players in Successful Implementation of ChatGPT and Similar Generative Artificial Intelligence in Manufacturing, Finance, Retail, Transportation, and Construction Industry. 2023. Available online: https://osf.io/preprints/osf/npm3d (accessed on 19 January 2024).
245. Voß, S. Bus Bunching and Bus Bridging: What Can We Learn from Generative AI Tools like ChatGPT? Sustainability 2023, 15 , 9625. [Google Scholar] [CrossRef]
246. Akhbardeh, F.; Desell, T.; Zampieri, M. NLP tools for predictive maintenance records in MaintNet. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing: System Demonstrations, Suzhou, China, 4–7 December 2020; pp. 26–32. [Google Scholar]
247. Singh, J.; Samborowski, L.; Mentzer, K. A Human Collaboration with ChatGPT: Developing Case Studies with Generative AI. In Proceedings of the ISCAP Conference on Information Systems and Computing Education, Albuquerque, NM, USA, 1–4 November 2023; ISSN 2473–4901. pp. 1–11. [Google Scholar]
248. Song, L.; Zhang, C.; Zhao, L.; Bian, J. Pre-Trained Large Language Models for Industrial Control. arXiv 2023, arXiv:2308.03028. [Google Scholar]
249. Frederico, G.F. ChatGPT in Supply Chains: Initial Evidence of Applications and Potential Research Agenda. Logistics 2023, 7 , 26. [Google Scholar] [CrossRef]
250. Wang, J.; Zhang, L.; Yang, Y.; Zhuang, Z.; Qi, Q.; Sun, H.; Lu, L.; Feng, J.; Liao, J. Network Meets ChatGPT: Intent Autonomous Management, Control and Operation. J. Commun. Inf. Netw. 2023, 8 , 239–255. [Google Scholar] [CrossRef]
251. Bodenhausen, U.; Braatz, A. Next Level AI-Based Development: From Understanding to Mastering of the Key Elements. In Proceedings of the International Stuttgart Symposium, Stuttgart, Germany, 26–27 September 2023; pp. 549–560. [Google Scholar]
252. Stewart, M.; Hodkiewicz, M.; Li, S. Large Language Models for Failure Mode Classification: An Investigation. arXiv 2023, arXiv:2309.08181. [Google Scholar]