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Faulty compressors must be timely detected to prevent excessive energy consumption, maintenance, and energy costs. Existing diagnostics models lack addressing the energy performance indicators and do not provide effective hybrid machine learning (ML) model for advanced fault diagnosis to prevent compressors from becoming energy hogs. Therefore, this study proposed a novel approach in the form of an energy-based diagnostic model for integrating energy performance indicators to detect the healthy and faulty behavior of the compressor using a hybrid ML model. The time series analysis and Isolation Forest techniques have been used to detect faulty and healthy behavior of centrifugal gas compressor. To obtain more insight and prevent false alarms the hybrid ML model was introduced. The Ridge regression was used as the meta-classifier in the suggested hybrid model, which receives the input from the base classifiers Decision Tree (DT), k-Nearest Neighbors (kNN), and Gradient Boosting (GB) to optimize the performance and accuracy of hybrid model. This study was conducted on a two-stage centrifugal compressor that compresses production gas for export powered by a gas turbine at Malaysia's PETRONAS Angsi oil and gas field. According to the findings, the energy efficiency predicted for the first stage was 84.2% for healthy behavior and 69.7% for faulty behavior, while for the second stage, it was 83.2% for healthy behavior and 68.1% for faulty behavior, indicating high energy efficiency during the healthy operation of a centrifugal compressor in comparison with faulty behavior. The slight difference between the proposed diagnostic model training, testing, and prediction performance accuracy 0.98, 0.97 and 0.99 proposes a model is efficient neither overfitting nor underfitting according to the value of co-efficient of determination (R2). The R2 values for training, testing, and prediction performance accuracy for the GB model were 0.95, 0.93, and 0.94; for kNN, 0.89, 0.87, and 0.86, and for Tree, 0.95, 0.94, and 0.93 respectively. According to the results, the proposed hybrid model performs more eloquently and efficiently than other single models DT, kNN, and GB. This study empowers operators to take critical measures to increase energy efficiency, reduce downtime, and schedule maintenance to improve the reliability of centrifugal gas compressor. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy. [ABSTRACT FROM AUTHOR]

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Predictive maintenance (PdM) allows the prediction of early failures of medical equipment before they occur. It helps to diagnose the defaults of critical equipment in a hospital structure, namely MRI. Founded on the analysis of data collected in real time of the right parameters, thanks to intelligent sensors positioned on the equipment, using Internet of Things (IoT) technology and the practice of machine learning tools. The objective of this techniques is the implementation of algorithms capable to predict an anomaly, which will make equipment and maintenance tools increasingly autonomous and intelligent. Therefore, the idea of this project is to develop a wireless sensor network to ensure continuous monitoring of the state of MRI. The implemented solution includes an IoT monitoring system of the cold head’s cooling circuit. Based on the vibrations at the pump, it allows to monitor the motor circuit, inform the staff at each abnormal state of this system, and protect this device against any future anomalies. Thanks to the CNN algorithm implemented in this solution, the results are very satisfactory, with an accuracy >98%. This solution can be integrated into a general predictive maintenance solution for the most sensitive equipment in a hospital.

Tool condition monitoring (TCM) is crucial to ensure good quality products and avoid downtime. Machine learning has proven to be vital for TCM. However, existing works are predominately based on supervised learning, which hinders their applicability in real-world manufacturing settings, where data labeling is cumbersome and costly with in-service machines. Additionally, the existing unsupervised solutions mostly handle binary decision-based TCM which is unable to fully reflect the dynamics of tool wear progression. To address these issues, we propose different unsupervised and semisupervised five-class tool wear recognition frameworks to handle fully unlabeled and partially labeled data, respectively. The underlying methods include Laplacian score, sparse autoencoder (SAE), stacked SAE (SSAE), self-organizing map, Softmax, support vector machine, and random forest. For the semisupervised frameworks, we considered designs where labeled data influence only feature learning, classifier building, or both. We also investigated different training configurations of SSAE regarding the supervision level. We applied the frameworks on two run-to-failure datasets of milling tools, recorded using a microphone and an accelerometer. Single sensor and multisensor data under different percentages of labeled training data were considered in the evaluation. The results showed which of the frameworks led to the best predictive performance under which data settings, and highlighted the significance of sensor fusion and discriminative feature representations in combating the unavailability and scarcity of labels, among other findings. The highest macro-F1 achieved for the two datasets with fully unlabeled data reached 87.52% and 75.80%, respectively, and over 90% when only 25% of the training observations were labeled.

Abstract This paper provides a comprehensive review on the Artificial Intelligence (AI) based models for predictive maintenance (PdM) of water injection pumps (WIPs) in the oil and gas industry (OGI). The review encompasses the selection of algorithms, data requirements, and optimization strategies, offering insights into advancements, challenges, and theoretical foundations including data pre-processing and feature selection. This review highlights AI-based PdM developments for WIPs, focusing on techniques and algorithms that enhance water injection pump performance and accurately predict maintenance needs. It emphasizes the effectiveness of algorithms in capturing pump data patterns and anomalies for proactive maintenance. Additionally, the review offers valuable insights for future research directions and practical implementations of AI in pump maintenance. This comprehensive assessment serves as a beacon for OGI experts in the selection of AI methods for pump maintenance, enabling them to refine their procedures, enhance efficiency, and reduce operational interruptions. A prominent highlight is the importance of data quality and interpretability, which play a pivotal role in facilitating well-informed decision-making during the integration of AI technologies into maintenance processes. This article focuses on the theoretical foundations of AI in the context of pump maintenance, providing a contribution to OGI industry. By integrating theoretical perspectives with real-world evidence, it offers insights for guiding future research and enhancing maintenance techniques. As a resource, it holds relevance for researchers, practitioners, and decision-makers within the OGI sector, contributing to the ongoing advancement of this field. Article Highlights Artificial Intelligence (AI) optimizes Oil & Gas pump maintenance for efficiency and reliability. Real-world case studies validate cost and time reductions, improving pump performance. Challenges in data management and ethical AI implementation require careful consideration.

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. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive review on the Artificial Intelligence (AI) based models for predictive maintenance (PdM) of water injection pumps (WIPs) in the oil and gas industry (OGI). The review encompasses the selection of algorithms, data requirements, and optimization strategies, offering insights into advancements, challenges, and theoretical foundations including data pre-processing and feature selection. This review highlights AI-based PdM developments for WIPs, focusing on techniques and algorithms that enhance water injection pump performance and accurately predict maintenance needs. It emphasizes the effectiveness of algorithms in capturing pump data patterns and anomalies for proactive maintenance. Additionally, the review offers valuable insights for future research directions and practical implementations of AI in pump maintenance. This comprehensive assessment serves as a beacon for OGI experts in the selection of AI methods for pump maintenance, enabling them to refine their procedures, enhance efficiency, and reduce operational interruptions. A prominent highlight is the importance of data quality and interpretability, which play a pivotal role in facilitating well-informed decision-making during the integration of AI technologies into maintenance processes. This article focuses on the theoretical foundations of AI in the context of pump maintenance, providing a contribution to OGI industry. By integrating theoretical perspectives with real-world evidence, it offers insights for guiding future research and enhancing maintenance techniques. As a resource, it holds relevance for researchers, practitioners, and decision-makers within the OGI sector, contributing to the ongoing advancement of this field. Article Highlights: Artificial Intelligence (AI) optimizes Oil & Gas pump maintenance for efficiency and reliability. Real-world case studies validate cost and time reductions, improving pump performance. Challenges in data management and ethical AI implementation require careful consideration. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) is implemented to efficiently manage maintenance schedules of machinery and equipment in manufacturing by predicting potential faults with advanced technologies such as sensors, data analysis, and machine learning algorithms. This paper introduces a study of different methodologies for automatically classifying the failures in PdM data. We first present the performance evaluation of fault classification performed by shallow machine learning (SML) methods such as Decision Trees, Support Vector Machines, k-Nearest Neighbors, and one-dimensional deep learning (DL) techniques like 1D-LeNet, 1D-AlexNet, and 1D-VGG16. Then, we apply normalization, which is a scaling technique in which features are shifted and rescaled in the dataset. We reapply classification algorithms to the normalized dataset and present the performance tables in comparison with the first results we obtained. Moreover, in contrast to existing studies in the literature, we generate balanced dataset groups by randomly selecting normal data and all faulty data for all fault types from the original dataset. The dataset groups are generated with 100 different repetitions, recording performance scores for each one and presenting the maximum scores. All methods utilized in the study are similarly employed on these groups. From these scores, the use of 1D-LeNet deep learning classifiers and feature normalization resulted in achieving the highest overall accuracy and F1-score performance of 98.50% and 98.32%, respectively. As a result, the goal of this study was to develop an efficient approach for automatic fault classification, leveraging data balance, and additionally, to provide an analysis of one-dimensional deep learning and shallow machine learning-based classification methods. In light of the experimentation and comparative analysis, this study successfully achieves its stated goal by demonstrating that one-dimensional deep learning and data balance collectively emerge as the optimal approach, offering good prediction accuracy.

Industry 4.0 has become a reality by fusing the Industrial Internet of Things (IIoT) and Artificial Intelligence (AI), providing huge opportunities in the way manufacturing companies operate. However, the adoption of this paradigm shift, particularly in the field of smart factories and production, is still in its infancy, suffering from various issues, such as the lack of high-quality data, data with high-class imbalance, or poor diversity leading to inaccurate AI models. However, data is severely fragmented across different silos owned by several parties for a range of reasons, such as compliance and legal concerns, preventing discovery and insight-driven IIoT innovation. Notably, valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security. This adversely influences inter- and intra-organization collaborative use of IIoT data. To tackle these challenges, this article leverages emerging multi-party technologies, privacy-enhancing techniques (e.g., Federated Learning), and AI approaches to present a holistic, decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape. Moreover, to evaluate the efficiency of the proposed reference model, a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture. Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable, Accessible, Interoperable, and Reusable (FAIR) principles.

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments. [ABSTRACT FROM AUTHOR]

Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) has become an important industrial feature. Existing methods mainly focus on remaining useful life (RUL) regression or anomaly detection to achieve PdM in a given application. Those approaches assume monotonic degradation processes leading to a single catastrophic failure at the system's end of lifetime. In contrast, much more complex degradation processes can be found in real-world applications, which are characterized by effects like self-healing or noncatastrophic anomalies. A important example of devices with complex degradation are electromechanical relays. As established PdM solutions failed when applied to a real-world relays degradation data set, the maintenance algorithm for unlabeled data (MAUD) is presented to detect signs of wear and enable a service in time. In detail, MAUD is based on an artificial neural network (ANN), which is trained semisupervised. Experiments with measurement data from 546 relays show that MAUD is superior to various existing methods: The static B10 threshold, which represents the state of the art in relay maintenance, is surpassed by a 17.07 p.p. increase in utilization while reducing failures by 6.42 p.p. Methods based on machine learning, such as RUL estimation and anomaly detection, achieved much lower utilization (up to 31.83 p.p.) compared with MAUD while maintaining the same failure rate.

Predictive maintenance (PdM) uses statistical and machine learning methods to detect and predict the onset of faults. PdM is often used in industrial IoT settings in the energy sector, where research works usually consider specific types of faults depending on the application. However, since PdM is mainly data-driven and needs to work in real time, the public availability of datasets is required in order to build efficient and effective models applicable across multiple domains. Unlike methods, the publicly available datasets obtained from sensors in the energy sector have not been properly reviewed or categorized. In this work, we consider five subsectors of the energy sector: wind, solar, oil & gas, diesel & thermal, and electrical power grid. We provide a detailed description of the properties of the publicly available PdM datasets in these subsectors. The review of the datasets is conducted on a number of scientific and commercial repositories: IEEE DataPort, UCI Machine Learning Repository, Kaggle, EDP, and Mendeley Data. The datasets are graded into three categories according to objective criteria. We also provide references to significant related research work that uses the considered datasets. The observed challenges in using the datasets in this field are thoroughly discussed. We find that there is a troublesome scarcity of publicly available datasets in the energy sector, more so of data coming from real, non-simulated sources. Three datasets, 3W (oil & gas), EDP-WT (wind), and OREC (wind) stand out as highly valuable for researchers in this field.

Predictive maintenance has revealed as one of the paradigms of Industry 4.0. This paper addresses a complete system for the acquisition, computing, monitoring and communication of ventilation equipment in underground tunnels based on TCP/IP protocol and accessible via WEB services. Not only does the proposed system collect different sensor data (temperatures, vibrations, pressures, tilt angles or rotational speed), it performs local data processing as well. This feature is the newest and most important of all those provided by the system design, and there is no equipment that offers a similar performance in current ventilation systems. This paper shows the design and implementation of the equipment (system architecture and processing), as well as the experimental results obtained. [ABSTRACT FROM AUTHOR]

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.

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review's main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel's feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement. [ABSTRACT FROM AUTHOR]

Datasets related to hard drive failure, particularly BackBlaze Hard Drive Data, have been widely studied in the literature using many statistical, machine learning, and deep learning techniques. These datasets are severely imbalanced due to the presence of a small number of failed drives compared to huge amounts of healthy drives in the operational data centers. It is challenging to mitigate the adverse consequence of the class imbalance due to the presence of bias towards the majority class during learning. SMART (self monitoring analysis and reporting technology) attributes of the disk drives were utilized in the past to design standard classification or regression algorithms. Although few machine learning (ML) models, for instance, tree based methods and ensemble learning algorithms, addressed the failure prediction, the effects of class imbalance were rarely properly considered under the ML framework. This study, based on a review of the state-of-the-art in the area, evaluates current methodologies to identify areas that were either overlooked or lacking, proposes methods for remediating these issues, and performs some baseline experiments to demonstrate the proposed methodologies including data sampling techniques and cost-sensitive learning.

Appropriate maintenance of industrial equipment keeps production systems in good health and ensures the stability of production processes. In specific production sectors, such as the electrical power industry, equipment failures are rare but may lead to high costs and substantial economic losses not only for the power plant but for consumers and the larger society. Therefore, the power production industry relies on a variety of approaches to maintenance tasks, ranging from traditional solutions and engineering know-how to smart, AI-based analytics to avoid potential downtimes. This review shows the evolution of maintenance approaches to support maintenance planning, equipment monitoring and supervision. We present older techniques traditionally used in maintenance tasks and those that rely on IT analytics to automate tasks and perform the inference process for failure detection. We analyze prognostics and health-management techniques in detail, including their requirements, advantages and limitations. The review focuses on the power-generation sector. However, some of the issues addressed are common to other industries. The article also presents concepts and solutions that utilize emerging technologies related to Industry 4.0, touching on prescriptive analysis, Big Data and the Internet of Things. The primary motivation and purpose of the article are to present the existing practices and classic methods used by engineers, as well as modern approaches drawing from Artificial Intelligence and the concept of Industry 4.0. The summary of existing practices and the state of the art in the area of predictive maintenance provides two benefits. On the one hand, it leads to improving processes by matching existing tools and methods. On the other hand, it shows researchers potential directions for further analysis and new developments.

Intelligence in Industry 4.0 has led to the development of smart buildings with various control systems for data collection, efficient optimization, and fault detection and diagnosis (FDD). However, buildings, especially with regard to heating, ventilation, and air conditioning (HVAC) systems, are responsible for significant global energy consumption. Digital Twin (DT) technology offers a sustainable solution for facility management. This study comprehensively reviews DT performance evaluation in building life cycle and predictive maintenance. 200 relevant papers were selected using a systematic methodology from Scopus, Web of Science, and Google Scholar, and various FDD methods were reviewed to identify their advantages and limitations. In conclusion, data-driven methods are gaining popularity due to their ability to handle large amounts of data and improve accuracy, flexibility, and adaptability. Unsupervised and semi-supervised learning as data-driven methods are important for FDD in building operations, such as with HVAC systems, as they can handle unlabeled data and identify complex patterns and anomalies. Future studies should focus on developing interpretable models to understand how the models made their predictions. Hybrid methods that combine different approaches show promise as reliable methods for further research. Additionally, deep learning methods can analyze large and complex datasets, indicating a promising area for further investigation.

Maintenance is the technology of continuously monitoring the conditions of equipment and predicting the timing of maintenance for equipment. Particularly in the field of semiconductor manufacturing, where processes are automated, various methods are being tried to minimize the economic losses and maintenance costs caused by equipment failure. A new Predictive Maintenance (PdM) technique, a new method of maintenance, is introduced in this paper to develop an algorithm for predicting the failure of wafer transfer robots in advance. The acceleration sensor data used in the experiment were obtained by installing a sensor onto the wafer transfer robot. To analyze these data, the data preprocessing and FFT process were performed. These data were divided into normal data, first error data, second error data, and third error data (failure data) in stages. By clustering the data using the K-means algorithm, the center point distribution of the clusters was analyzed, and the features of the error data and normal data were extracted. Using these features, an artificial neural network model was designed to predict the point of failure of the robot. Previous research on maintenance systems of the transfer robot used fewer than 50 error data, but 1686 error data were used in this experiment. The reliability of the model is improved by randomly selecting data from a total of 2248 data sets. In addition, it was confirmed that it was possible to classify normal data and error data with an accuracy of 97% and to predict equipment failure by applying neural network modeling. [ABSTRACT FROM AUTHOR]

The proliferation of sensing technologies such as sensors has resulted in vast amounts of time-series data being produced by machines in industrial plants and factories. There is much information available that can be used to predict machine breakdown and degradation in a given factory. The downtime of industrial equipment accounts for heavy losses in revenue that can be reduced by making accurate failure predictions using the sensor data. Internet of Things (IoT) technologies have made it possible to collect sensor data in real time. We found that hybrid modelling can result in efficient predictions as they are capable of capturing the abstract features which facilitate better predictions. In addition, developing effective optimization strategy is difficult because of the complex nature of different sensor data in real time scenarios. This work proposes a method for multivariate time-series forecasting for predictive maintenance (PdM) based on a combination of convolutional neural networks and long short term memory with skip connection (CNN-LSTM). We experiment with CNN, LSTM, and CNN-LSTM forecasting models one by one for the prediction of machine failures. The data used in this experiment are from Microsoft's case study. The dataset provides information about the failure history, maintenance history, error conditions, and machine features and telemetry, which consists of information such as voltage, pressure, vibration, and rotation sensor values recorded between 2015 and 2016. The proposed hybrid CNN-LSTM framework is a two-stage end-to-end model in which the LSTM is leveraged to analyze the relationships among different time-series data variables through its memory function, and 1-D CNNs are responsible for effective extraction of high-level features from the data. Our method learns the long-term patterns of the time series by extracting the short-term dependency patterns of different time-series variables. In our evaluation, CNN-LSTM provided the most reliable and highest prediction accuracy. [ABSTRACT FROM AUTHOR]

Predictive maintenance (PdM) is an effective means to eliminate potential failures, ensure stable equipment operation and improve the mission reliability of manufacturing systems and the quality of products, which is the premise of intelligent manufacturing. Therefore, an integrated PdM strategy considering product quality level and mission reliability state is proposed regarding the intelligent manufacturing philosophy of ‘prediction and manufacturing’. First, the key process variables are identified and integrated into the evaluation of the equipment degradation state. Second, the quality deviation index is defined to describe the quality of the product quantitatively according to the co-effect of manufacturing system component reliability and product quality in the quality–reliability chain. Third, to achieve changeable production task demands, mission reliability is defined to characterise the equipment production states comprehensively. The optimal integrated PdM strategy, which combines quality control and mission reliability analysis, is obtained by minimising the total cost. Finally, a case study on decision-making with the integrated PdM strategy for a cylinder head manufacturing system is presented to validate the effectiveness of the proposed method. The final results shows that proposed method achieves approximately 26.02 and 20.54% cost improvement over periodic preventive maintenance and conventional condition-based maintenance respectively. [ABSTRACT FROM PUBLISHER]

In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data from service history, logs, metrics, transactions, and plausible environmental factors. This study helps increase device availability, enhanced customer experience, manage risk & compliance and revenue growth. It also helps reduce maintenance cost, travel cost, labour cost, downtime, repair duration and increase meantime between failures (MTBF) of individual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence on machine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, and c) A forward-looking evaluation of emerging patterns to determine failure prediction score that is prioritized by business impact, for a predefined time window in the future. The model not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows.

In a semiconductor plasma etcher, it is becoming increasingly necessary to improve productivity by reducing unplanned equipment maintenance. Thus, predictive maintenance (PdM) is typically conducted using equipment data to predict the failure timing, after which proactive measures should be taken. In PdM, the planned maintenance schedule is updated on the basis of the predicted failure timing. However, in practice, the predicted failure timing has a probabilistic variability. Therefore, we propose a maintenance schedule update method based on the expected maintenance cost calculated from the probabilistic variability of the failure timing. We applied our method and conventional methods to a dataset of failure cases that model actual component failures of etchers and found that our method was effective in terms of reducing maintenance costs. [ABSTRACT FROM AUTHOR]

The importance of predictive maintenance (PdM) programs has been recognized across many industries. Seamless integration of the PdM program into today’s manufacturing execution systems requires a scalable and generic system design and a set of key performance indicators (KPIs) to make condition monitoring and PdM activities more effective. In this study, a new PdM system and its implementation are presented. KPIs and metrics are proposed and implemented during the design to enhance the system and the PdM performance monitoring needs. The proposed system has been tested in two independent use cases (autonomous transfer vehicle and electric motor) for condition monitoring applications to detect incipient equipment faults or operational anomalies. Machine learning-based data augmentation tools and models are introduced and automated with state-of-the-art AutoML and workflow automation technologies to increase the system’s data collection and data-driven fault classification performance.

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.

Predictive maintenance is an essential aspect of microgrid operations as it enables identifying potential equipment failures in advance, reducing downtime, and increasing the overall efficiency of the system. Machine learning-based techniques have a great potential to be effective in improving the accuracy of failure predictions, detecting, and diagnosing faults in real-time, and monitoring the health and remaining useful life of microgrid components. The integration of these techniques with microgrid components can lead to reduced downtime, improved safety, overall efficiency, and sustainability. This work aims to explore the research scope of machine learning-based predictive maintenance in microgrid systems. The analysis provides a comprehensive review of the state-of-the-art machine learning techniques that could be used for microgrid predictive maintenance and highlights the gaps and challenges that need to be addressed. This study suggests future research directions in the field and frameworks to improve predictive maintenance using machine learning for microgrid industries. [Display omitted] • Scopes of machine learning based microgrid predictive maintenance. • Framework for machine learning based microgrid predictive maintenance. • Analysis of machine learning methods in the context of microgrid components. • Exploring microgrid data sources & public datasets. • Recommendations for integrating latest, advanced machine learning algorithms. [ABSTRACT FROM AUTHOR]

In this technical paper, we address the issue of predicting cash dispenser (addressed as ‘Device’ henceforth) failure by harnessing the power of humungous data from service history, logs, metrics, transactions, and plausible environmental factors. This study helps increase device availability, enhanced customer experience, manage risk & compliance and revenue growth. It also helps reduce maintenance cost, travel cost, labour cost, downtime, repair duration and increase meantime between failures (MTBF) of individual components. This study uses a cognitive prioritization model which entails the following at its core; a) Machine Learning engineered features with highest influence on machine failure, b) Observation Windows, Transition Windows and Prediction Windows to accommodate various business processes and service planning delivery windows, and c) A forward-looking evaluation of emerging patterns to determine failure prediction score that is prioritized by business impact, for a predefined time window in the future. The model not only predicts failure score for the devices to be serviced, but it also reduces the service miss impact for the prediction windows. [ABSTRACT FROM AUTHOR]

In this article, we present a low-power (LP) anomaly detection integrated circuit (ADIC) based on a one-class classifier (OCC) neural network. The ADIC achieves LP operation through a combination of: 1) careful choice of algorithm for online learning and 2) approximate computing techniques to lower average energy. In particular, online pseudoinverse update method (OPIUM) is used to train a randomized neural network for quick and resource-efficient learning. An additional 42% energy saving can be achieved when a lighter version of OPIUM method is used for training with the same number of data samples lead to no significant compromise on the quality of inference. Instead of a single classifier with large number of neurons, an ensemble of $K$ base learner (BL) approach is chosen to reduce learning memory by a factor of $K$. This also enables approximate computing by dynamically varying the neural network size based on anomaly detection. Fabricated in 65-nm CMOS, the ADIC has $K=7$ BLs with 32 neurons in each BL and dissipates 11.87 and 3.35 pJ/OP during learning and inference, respectively, at $V_{\text {dd}}=0.75\,\,\text {V}$ when all seven BLs are enabled. Furthermore, evaluated on the NASA bearing data set, approximately 80% of the chip can be shut down for 99% of the lifetime leading to an energy efficiency of 0.48 pJ/OP, an $18.5 \times $ reduction over full-precision computing running at $V_{\text {dd}}=1.2 \,\, \text {V}$ throughout the lifetime. [ABSTRACT FROM AUTHOR]

Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section. [ABSTRACT FROM AUTHOR]

With the tremendous revival of artificial intelligence, predictive maintenance (PdM) based on data-driven methods has become the most effective solution to address smart manufacturing and industrial big data, especially for performing health perception (e.g., fault diagnosis and remaining life assessment). Moreover, because the existing PdM research is still in primary experimental stage, most works are conducted utilizing several open-datasets, and the combination with specific applications such as rotating machinery is especially rare. Hence, in this paper, we focus on data-driven methods for PdM, present a comprehensive survey on its applications, and attempt to provide graduate students, companies, and institutions with the preliminary understanding of the existing works recently published. Specifically, we first briefly introduce the PdM approach, illustrate our PdM scheme for automatic washing equipment, and demonstrate the challenges encountered when we conduct a PdM research. Second, we classify the specific industrial applications based on six algorithms of machine learning and deep learning (DL), and compare five performance metrics for each classification. Furthermore, the accuracy (a metric to evaluate the algorithm performance) of these PdM applications is analyzed in detail. There are some important conclusions: 1) the data used in the summarized literature are mostly from public datasets, such as case western reserve university (CWRU)/intelligent maintenance systems (IMS); and 2) in recent years, researchers seem to focus more on DL algorithms for PdM research. Finally, we summarize the common features regarding our surveyed PdM applications and discuss several potential directions. [ABSTRACT FROM AUTHOR]

The success of all industries relates to attaining the satisfaction to clients with a high level of services and productivity. The success main factor depends on the extent of maintaining their equipment. To date, the Rwandan hospitals that always have a long queue of patients that are waiting for service perform a repair after failure as common maintenance practice that may involve unplanned resources, cost, time, and completely or partially interrupt the remaining hospital activities. Aiming to reduce unplanned equipment downtime and increase their reliability, this paper proposes the Predictive Maintenance (PdM) structure while using Internet of Things (IoT) in order to predict early failure before it happens for mechanical equipment that is used in Rwandan hospitals. Because prediction relies on data, the structure design consists of a simplest developed real time data collector prototype with the purpose of collecting real time data for predictive model construction and equipment health status classification. The real time data in the form of time series have been collected from selected equipment components in King Faisal Hospital and then later used to build a proposed predictive time series model to be employed in proposed structure. The Long Short Term Memory (LSTM) Neural Network model is used to learn data and perform with an accuracy of 90% and 96% to different two selected components.

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Considering the operation process rebuilding (OPR) of manufacturing/operation systems, we propose a dynamic interactive bilevel maintenance methodology to satisfy rapid market changes. Predictive maintenance (PdM) intervals at the machine level are dynamically scheduled by a multiobjective model for each diverse machine. A system-level opportunistic maintenance (OM) policy is proposed to facilitate PdM optimizations according to OPR activities. This novel OPR-OM policy utilizes a variable maintenance time window to construct optimal maintenance schedules that are suitable for changeable system structures. The results obtained by applying this methodology at Shanghai Port indicate that the proposed methodology can help a port transportation system to achieve rapid responses to OPR activities, which can significantly improve system efficiency and economy. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) estimation, a key component in predictive maintenance (PdM), aims to reduce maintenance cycles in the prognostic health of mechanical equipment(s). Research directions using deep-learning-based RUL estimation often suffer from limited availability of degradation signals resulting inaccurate predictions. Until now, state-of-the-art models trained on large sets of natural images to classify objects have not been re-used to improve regression-based RUL estimation accuracies of mechanical equipment. Actually, this is a rarely researched topic in PdM. Inspired by transfer learning, we showcase that the knowledge learned by popular pre-trained models can be transferred to improve industrial machinery-based-maintenance decision-making. Accordingly, this paper proposes a novel multi-model data-fusion-based deep transfer learning (MMF-DTL) framework for improved RUL estimation of rolling bearings through degradation images (DI) and pre-trained deep convolutional neural networks (CNNs). After procuring the degradation signals, we obtain DIs incorporating sufficient deterioration information using Markov Transition Fields. Next, these DIs are input into a DTL network comprising three pre-trained CNNs, i.e., DenseNet201, VGG16, and ResNet50, designed in a parallel fashion, where each constituent fine-tunes a different count of layers. Subsequently, features extracted from each component model are fused in a weighted manner and passed onto several fully connected layers. The effectiveness of the proposed framework is validated using the PHM Challenge 2012 bearing degradation dataset. Compared to several state-of-the-art works, our approach improves ∼ 12. 57 % on error rate and ∼ 26. 04 % on MAE, suggesting it is practically feasible to grasp transferable attributes from a general-purpose related dataset to another with minimal dataset size. [ABSTRACT FROM AUTHOR]