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Anomaly detection is a frequently discussed topic in manufacturing. However, the issues of anomaly detection are typically attributed to the manufacturing process or equipment itself. In practice, the sensor responsible for collecting data and monitoring values may fail, leading to a biased detection result – false alarm. In such cases, replacing the sensor is necessary instead of performing equipment maintenance. This study proposes an effective framework embedded with autoencoder-based control limits that can dynamically distinguish sensor anomaly from process anomaly in real-time. We conduct a simulation numerical study and an empirical study of semiconductor assembling manufacturers to validate the proposed framework. The results show that the proposed model outperforms other benchmark methods and can successfully identify sensor failures, even under conditions of (1) large variations in process values or sensor values and (2) heteroscedasticity effect. This is particularly beneficial in various practical applications where sensors are used for numerical measurements and support equipment maintenance. [ABSTRACT FROM AUTHOR]

Data-driven artificial intelligence models require explainability in intelligent manufacturing to streamline adoption and trust in modern industry. However, recently developed explainable artificial intelligence (XAI) techniques that estimate feature contributions on a model-agnostic level such as SHapley Additive exPlanations (SHAP) have not yet been evaluated for semi-supervised fault diagnosis and prognosis problems characterized by class imbalance and weakly labeled datasets. This paper explores the potential of utilizing Shapley values for a new clustering framework compatible with semi-supervised learning problems, loosening the strict supervision requirement of current XAI techniques. This broad methodology is validated on two case studies: a heatmap image dataset obtained from a semiconductor manufacturing process featuring class imbalance, and the benchmark N-CMAPSS dataset. Semi-supervised clustering based on Shapley values significantly improves upon clustering quality compared to the fully unsupervised case, deriving information-dense and meaningful clusters that relate to underlying fault diagnosis model predictions. These clusters can also be characterized by high-precision decision rules in terms of original feature values, as demonstrated in the second case study. The rules, limited to 2 terms utilizing original feature scales, describe 14 out of the 19 derived equipment failure clusters with average precision exceeding 0.85, showcasing the promising utility of the explainable clustering framework for intelligent manufacturing applications. [ABSTRACT FROM AUTHOR]

Bearings are pivotal components of rotating machines where any defects could propagate and trigger systematic failures. Once faults are detected, accurately predicting remaining useful life (RUL) is essential for optimizing predictive maintenance. Although data-driven methods demonstrate promising performance in direct RUL prediction, their robustness and practicability need further improvement regarding physical interpretation and uncertainty quantification. This work leverages variational neural networks to model bearing degradation behind envelope spectra. A convolutional variational autoencoder for regression (CVAER) is developed to probabilistically predict RUL distributions with confidence measures. Enhanced average envelope spectra (AES) are used as network input for its physical robustness in bearing condition assessment and fault detection. The use of the envelope spectrum ensures that it contains only bearing-related information by removing other rotor-related frequencies, hence it improves the RUL prediction. Unlike traditional variational autoencoders, the probabilistic regressor and latent generator are formulated to quantify uncertainty in RUL estimates and learn meaningful latent representations conditioned on specific RUL. Experimental validations are conducted on vibration data collected using multiple accelerometers whose natural frequencies cover bearing resonance ranges to ensure fault detection reliability. Beyond conventional bearing diagnosis, envelope spectra are extended for statistical RUL prediction integrating physical knowledge of actual defect conditions. Comparative and ablation studies are conducted against benchmark models to demonstrate their effectiveness. [ABSTRACT FROM AUTHOR]

The zinc oxide (ZnO) arrester is positioned on the locomotive roof, preventing other onboard electrical equipment from being harmed by external and internal overvoltages. A number of locomotive arrester burnout and explosion accidents occur during traction supply network harmonic resonance, causing train stoppages and power outages in the network. This study first carries out high-frequency tests to investigate the wide frequency band electrical characteristics of the locomotive arrester and prove that the arrester is prone to fault during resonance. Consequently, a prognostics and health management (PHM) methodology for locomotive arresters is proposed, providing online monitoring, fault warning, and health assessment, which is beneficial to the operation and maintenance of arresters in the future. [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this study, a novel Prognostics and Health Management (PHM) study was conducted to face these challenges. Firstly, a physical P2 HEV model with a rule-based controller was built, and its diesel engine sub-model was simplified by a neural network (NN) to ensure real-time performance of the degradation prognostics. Secondly, a degradation prognostics method based on gray relation analysis–principal component analysis (GRA-PCA) was illustrated, which could confirm degradation 2 s after the health index fell below the threshold. Finally, a degradation tolerance strategy based on long short term memory–model predictive control (LSTM-MPC) was performed to optimize vehicle speed tracing with minimal energy consumption and was validated by three cases. The result shows that the energy consumption stayed nearly unchanged for the engine degradation case. For the battery degradation case, the tracing error was reduced by 11.7% with 4.3% more energy consumption. For combined degradation, the strategy achieved a 12.3% tracing error reduction with 3.7% more energy consumption. The suggested PHM method guaranteed vehicle power performance under degradation situations. [ABSTRACT FROM AUTHOR]

In response to the "difficult to integrate" of full lifecycle data, "difficult to quantify" of health prediction and evaluation, "difficult to optimise" of maintenance decisions, and "difficult to generalise" of system platforms in the prognostics and health management (PHM) of large rotating machinery, cloud native thinking is introduced, containerization technology and microservice architecture are adopted, and an open universal architecture of the system software for PHM of large rotating machinery. The universal platform of system software for PHM of large-scale rotating machinery is developed, including a backend universal development engine and personalized customized application programming interface (API) interface for the front end. The "1 + 3" agile customized development strategy of application software for PHM of large rotating machinery has been proposed, which based on a universal platform, and integrated three key aspects: data access of application objects, encapsulation and deployment of algorithmic models, and visualization design, to achieve agile development of application software for PHM of large rotating machinery. By adopting an open and win-win approach, more model algorithm developers can participate in building bank of algorithm for PHM, and utilizing the collaborative mechanism of system software to solve the challenge of integrating data throughout the entire lifecycle, the foundation for constructing ecosystem of application software developing of PHM for large-scale rotary machinery will be laid. [ABSTRACT FROM AUTHOR]

Abstract Prognostics and health management (PHM) has gotten considerable attention in the background of Industry 4.0. Battery PHM contributes to the reliable and safe operation of electric devices. Nevertheless, relevant reviews are still continuously updated over time. In this paper, we browsed extensive literature related to battery PHM from 2018 to 2023 and summarized advances in battery PHM field, including battery testing and public datasets, fault diagnosis and prediction methods, health status estimation and health management methods. The last topic includes state of health estimation methods, remaining useful life prediction methods and predictive maintenance methods. Each of these categories is introduced and discussed in details. Based on this survey, we accordingly discuss challenges left to battery PHM, and provide future research opportunities. This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners.

Addressing the need for multiple health indicators is critical to improving prediction accuracy and reducing the limitation of reliance on a single health indicator. This paper presents an AutoML model for accurately forecasting the life span of lithium-ion batteries (LIBs) in electric vehicles. Unlike previous studies focusing solely on constant current (CC) and constant voltage (CV) charging, our AutoML approach includes ICA and direct analysis to generate eight novel multi-dimensional health indicators. These novel comprehensive indicators characterizing battery aging, including the position values of CC charging (IC_CC_P), the peak of CV charging (IC_CV_ H), and the peak/position of CC discharging (IC_D_H, IC_D_ P), are chosen based on correlation analysis and combined with four health indicators from direct analysis, i.e., capacity, cycle, cluster, and checkpoint, to improve prediction accuracy. K-means clustering is employed to group similar data points. At the same time, the ruptures library facilitates the implementation of a novel algorithm for change point detection, allowing for the identification of checkpoints within each battery dataset. Experimental validation using NASA's AMES LIB cycle life datasets demonstrates a significant performance improvement from existing recent methods, with 58.93 % lower mean absolute error (MAE) and 52.66 % lower root-mean-square error (RMSE) compared to recent AutoML-based methods. This shows a significant improvement in forecasting the RUL of electric vehicle LIBs using the proposed AutoML-based approach with novel multi-dimensional health indicators. [ABSTRACT FROM AUTHOR]

Remaining Useful Life (RUL) prediction has received a lot of attention as the core of prognostics and health management (PHM) technology. Deep learning-based RUL prediction methods are currently the most popular, and in order to solve the problem that most of the current deep RUL prediction studies do not consider the structural information between sensors, we propose a spatiotemporal autoencoder (STAE)-based RUL prediction method. The method extracts the time domain information from the data through the temporal convolutional network. It obtains the structural information of the sensors by converting the time series data into a graph structure by utilizing the maximal information coefficient and then performing the graph representation learning. For the two obtained features, a feature fusion method based on the graph attention mechanism is used for fusion and finally, the new fused features are utilized for RUL prediction. To validate the effectiveness of STAE, we conducted experiments on the simulated dataset C-MAPSS and the real satellite dataset SCS-PSS, and our proposed method outperforms the baseline method on both datasets. The results suggest that considering structural information between sensors in the deep RUL prediction model can improve prediction accuracy. [ABSTRACT FROM AUTHOR]

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein–Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model's input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems. [ABSTRACT FROM AUTHOR]

A new method for evaluating aircraft engine monitoring data is proposed. Commonly, prognostics and health management systems use knowledge of the degradation processes of certain engine components together with professional expert opinion to predict the Remaining Useful Life (RUL). New data-driven approaches have emerged to provide accurate diagnostics without relying on such costly processes. However, most of them lack an explanatory component to understand model learning and/or the nature of the data. A solution based on a novel recurrent version of a VAE is proposed in this paper to overcome this gap. The latent space learned by the model, trained with data from sensors placed in different parts of these engines, is exploited to build a self-explanatory map that can visually evaluate the rate of deterioration of the engines. Besides, a simple regressor model is built on top of the learned features of the encoder in order to numerically predict the RUL. As a result, remarkable prognostic accuracy is achieved, outperforming most of the novel and state-of-the-art approaches on the available modular aero-propulsion system simulation data (C-MAPSS dataset) from NASA. In addition, a practical real-world application is included for Turbofan engine data. This study shows that the proposed prognostic and explainable framework presents a promising new approach. [ABSTRACT FROM AUTHOR]

Prognostics and health management (PHM) has gotten considerable attention in the background of Industry 4.0. Battery PHM contributes to the reliable and safe operation of electric devices. Nevertheless, relevant reviews are still continuously updated over time. In this paper, we browsed extensive literature related to battery PHM from 2018 to 2023 and summarized advances in battery PHM field, including battery testing and public datasets, fault diagnosis and prediction methods, health status estimation and health management methods. The last topic includes state of health estimation methods, remaining useful life prediction methods and predictive maintenance methods. Each of these categories is introduced and discussed in details. Based on this survey, we accordingly discuss challenges left to battery PHM, and provide future research opportunities. This research systematically reviews recent research about battery PHM from the perspective of key PHM steps and provide some valuable prospects for researchers and practitioners. [ABSTRACT FROM AUTHOR]

With the rapid development of science and technology, the integration and complexity of aerospace vehicles, weaponry, and large-scale chemical equipment are becoming higher and higher. PHM plays an important role in realizing reductions in equipment loss due to failures in many fields. In order to systematically sort through the research history of PHM and deeply analyze the development status of AR and DT technologies in the field of PHM, to clarify the current technical challenges and future development directions and to provide valuable references and insights for researchers, engineers, and decision-makers in the related fields, this paper summarizes the development of PHM in the engineering field. This paper summarizes the development of PHM in the field of engineering, from the initial PHM used in aerospace to the current PHM systems supported by various advanced technologies; analyzes the advantages and shortcomings of the digital twin and augmented reality technologies used for PHM; and organizes and summarizes the future directions and future research focuses of PHM research based on the existing technologies (mainly digital twins and augmented reality). After systematic research and study, we found that the integration of augmented reality and digital twin technologies will provide superb simulation capabilities and immersive operation and bring new challenges and opportunities. Therefore, it is also imperative to address the challenges and limitations that hinder the seamless integration of the new technologies. [ABSTRACT FROM AUTHOR]

Rotorcrafts experience severe vibrations during operation. To ensure the safety of rotorcrafts, it is necessary to perform anomaly detection to detect small-scale structural faults in major components. To accurately detect small-scale faults before they grow to a fatal size, HR (high sampling rate) vibration data are required. However, to increase the efficiency of data storage media, only LR (low sampling rate) vibration data are generally collected during actual flight operation. Anomaly detection using only LR data can detect faults above a certain size, but may fail to detect small-scale faults. To address this problem, we propose an anomaly detection technique using the SR3 (Super-Resolution via Repeated Refinement) algorithm to upscale LR data to HR data, and then applying the LSTM-AE model. This technique is validated for two datasets (drone arm data, CWRU bearing data). First, the necessity for HR data is illustrated by showing that anomaly detection using LR data fails, and the upscaling performance of the SR3 algorithm is validated in the frequency and time domain. Finally, the anomaly detection for a structural fault diagnosis is performed for the upscaled data and the HR data using the LSTM-AE model. The quantitative evaluation of the Min–Max normalized reconstruction error distribution is performed through the MSE (Mean Square Error) value of the anomaly detection results. As a result, it is confirmed that the anomaly detection using upscaled test data can be performed as successfully as the anomaly detection using HR test data for both datasets by the proposed technique. [ABSTRACT FROM AUTHOR]

Recent developments in condition-based maintenance (CBM) have helped make it a promising approach to maintenance cost avoidance in engineering systems. By performing maintenance based on conditions of the component with regards to failure or time, there is potential to avoid the large costs of system shutdown and maintenance delays. However, CBM requires a large investment cost compared to other available maintenance strategies. The investment cost is required for research, development, and implementation. Despite the potential to avoid significant maintenance costs, the large investment cost of CBM makes decision makers hesitant to implement. This study is the first in the literature that attempts to address the problem of conducting a cost-benefit analysis (CBA) for implementing CBM concepts for unmanned systems. This paper proposes a method for conducting a CBA to determine the return on investment (ROI) of potential CBM strategies. The CBA seeks to compare different CBM strategies based on the differences in the various maintenance requirements associated with maintaining a multi-component, unmanned system. The proposed method uses modular dynamic fault tree analysis (MDFTA) with Monte Carlo simulations (MCS) to assess the various maintenance requirements. The proposed method is demonstrated on an unmanned surface vessel (USV) example taken from the literature that consists of 5 subsystems and 71 components. Following this USV example, it is found that selecting different combinations of components for a CBM strategy can have a significant impact on maintenance requirements and ROI by impacting cost avoidances and investment costs.

Remaining useful life prediction models are a central aspect of developing modern and capable prognostics and health management systems. Recently, such models are increasingly data-driven and based on various machine learning techniques, in particular deep neural networks. Such models are notoriously “data hungry”, i.e., to get adequate performance of such models, a substantial amount of diverse training data is needed. However, in several domains in which one would like to deploy data-driven remaining useful life models, there is a lack of data or data are distributed among several actors. Often these actors, for various reasons, cannot share data among themselves. In this paper a method for collaborative training of remaining useful life models based on federated learning is presented. In this setting, actors do not need to share locally held secret data, only model updates. Model updates are aggregated by a central server, and subsequently sent back to each of the clients, until convergence. There are numerous strategies for aggregating clients’ model updates and in this paper two strategies will be explored: 1) federated averaging and 2) federated learning with personalization layers. Federated averaging is the common baseline federated learning strategy where the clients’ models are averaged by the central server to update the global model. Federated averaging has been shown to have a limited ability to deal with non-identically and independently distributed data. To mitigate this problem, federated learning with personalization layers, a strategy similar to federated averaging but where each client is allowed to append custom layers to their local model, is explored. The two federated learning strategies will be evaluated on two datasets: 1) run-to-failure trajectories from power cycling of silicon-carbide metal-oxide semiconductor field-effect transistors, and 2) C-MAPSS, a well-known simulated dataset of turbofan jet engines. Two neural network model architectures commonly used in remaining useful life prediction, long short-term memory with multi-layer perceptron feature extractors, and convolutional gated recurrent unit, will be used for the evaluation. It is shown that similar or better performance is achieved when using federated learning compared to when the model is only trained on local data.

Predicting the remaining useful life (RUL) of an engine is one of the key tasks of Prognostics and health management (PHM). Modern mechanical equipment typically operates in complex operating conditions and fault modes, leading to dispersed distribution of sensor data and challenges for feature extraction. To improve the accuracy of predicting the RUL under the complex scenarios, this paper proposes a multi‐scale convolutional network (CNN) and bidirectional gated recurrent unit (MSC‐BiGRU) mode under a dual path framework with temporal attention. Specifically, the multi‐scale CNN in the first path is to learn complex features, and Swish Activation function is used to improve the prediction ability of the network; the bidirectional gated recurrent unit (BiGRU) in the second path can handle both forward and backward time series, and adaptively capture the importance of outputs at different times using temporal attention, enhancing the model's feature extraction ability in the temporal dimension. A feature fusion mechanism is developed to connect two paths in parallel, overcoming the overfitting and high computational complexity in deep complex models. We verify the effectiveness of the proposed method using a simulated turbofan engine dataset, especially on datasets FD002 and FD004 under complex operating conditions and fault modes, the RMSE values were reduced by 17.37% and 9.97%, respectively, compared to the BiGRU‐TSAM. [ABSTRACT FROM AUTHOR]

Machining of single-phase tungsten, used as a plasma facing material in fusion energy reactors, is commonly associated with rapid tool wear and short tool life. Conventional methods of monitoring tool wear or changing cutting tools after a predetermined period are inefficient and can lead to unnecessary tool change or risk damaging the workpiece. Tool wear can adversely affect the surface finish and dimensional tolerances of machined parts. Predicting its onset can avoid this critical damage whilst ensuring maximum tool life is utilised. In this paper, firstly the tool life results in end milling single-phase tungsten using different cutting tool geometries and cutting speeds are provided for the first time. A novel method is proposed by combining sensor signal prediction and classification machine learning models. It works by forecasting the cutting tool bending moment signal which is then used for predicting future cutting tool condition in end milling of pure dense tungsten. A series of machining experiments, covering the whole life of a cutting tool, were performed to collect the sensor signals. The current time series signal from the sensory tool holder is employed to forecast the future signal by training a 1D convolutional neural network (1D CNN) and an artificial neural network (ANN). The forecasted signal is then used to predict the state of the cutting tool in the future. Machine learning classifiers namely, random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGBoost) supervised learning models were trained and validated on actual sensor signals to correlate the tool conditions with specific sensor signal features. The investigations revealed that the 1D CNN performed best in forecasting the time series sensor signal whilst achieving a mean absolute error of 3.37. In addition, the RF, when trained on Wavelet Scattering features, resulted in the most accurate classification of sensor signals for tool condition detection. The analysis showed that the combination of 1D CNN signal forecasting, feature extraction through statistical analyses and RF classifier performs best in predicting the state of a cutting tool in near future. Using this method allows for decision making for changing the tool whilst ensuring that the maximum useful life of a cutting tool is utilised. It also enables preventing undesired damage to the machined surface due to late detection of tool wear or delays in taking appropriate actions. The application of this method can reliably reduce the manufacturing costs and resource consumption associated with cutting tools for machining tungsten and minimise tool wear induced damage to the workpiece. [ABSTRACT FROM AUTHOR]

Industrial systems operating under harsh and stochastic conditions are vulnerable to anomalies that degrade its performance and subsequently lead to unexpected breakdown. With the advent of Internet of Things (IoT), intelligent sensors have enabled maintenance managers to collect system data and analyze its behavior accurately in real-time. Based on this, many data-driven early fault detection approaches have been developed that try to detect anomalies associated with impending faults. Despite its advantages, only limited and scattered applications of anomaly detection approaches can be seen in system health monitoring of mechanical systems. One of the possible reasons could be scarcity of a comprehensive literature review that presents evolution of the field, highlighting key challenges and open questions to be addressed for future developments. This study narrows this gap by presenting a state-of-art review of data-driven approaches employed to early fault detection of various industrial systems. After critical analysis, challenges found in the previous studies and open questions for future research are also discussed. This study can be a reference point for researchers interested in addressing the critical challenges faced by maintenance practitioners in the industry. [ABSTRACT FROM AUTHOR]

This paper proposes an ontology for prognostics and health management (PHM) applications involving sensor networks monitoring industrial machinery. Deep learning methods show promise for the development of autonomous PHM systems but require vast quantities of structured and representative data to realize their potential. PHM systems involve unique and specialized data characterized by time and context, and thus benefit from tailored data management systems. Furthermore, the use of dissimilar standards and practices with respect to database structure and data organization is a hinderance to interoperability. To address this, this paper presents a robust, structured data ontology and schema that is designed to accommodate a wide breadth of PHM applications. The inclusion of contextual and temporal data increases its value for developing and deploying enhanced ML-driven PHM systems. Challenges around balancing the competing priorities of structure and flexibility are discussed. The proposed schema provides the benefits of a relational schema with some provisions for noSQL-like flexibility in areas where PMH applications demand it. The selection of a database engine for implementation is also discussed, and the proposed ontology is demonstrated using a Postgres database. An instance of the database was loaded with large auto-generated fictitious data via multiple Python scripts. CRUD (create, read, update, delete) operations are demonstrated with several queries that answer common PHM questions. [ABSTRACT FROM AUTHOR]

In the field of Prognostics and Health Management (PHM), recent years have witnessed a significant surge in the application of machine learning (ML). Despite this growth, the field grapples with a lack of unified guidelines and systematic approaches for effectively implementing these ML techniques and comprehensive analysis regarding industrial open-source data across varied scenarios. To address these gaps, this paper provides a comprehensive review of ML approaches for diagnostics and prognostics of industrial systems using opensource datasets from PHM Data Challenge Competitions held between 2018 and 2023 by PHM Society and IEEE Reliability Society and summarizes a unified ML framework. This review systematically categorizes and scrutinizes the problems, challenges, methodologies, and advancements demonstrated in these competitions, highlighting the evolving role of both conventional machine learning and deep learning in tackling complex industrial tasks related to detection, diagnosis, assessment, and prognosis. Moreover, this paper delves into the common challenges in PHM data challenge competitions by emphasizing data-related and model-related issues and evaluating the limitations of these competitions. The potential solutions to address these challenges are also summarized. Finally, we identify key themes and potential directions for future research, providing opportunities and prospects for next-generation ML-PHM development in PHM domain.

Rolling element bearing degradation prediction is an important issue in rotating machinery. With the rapid development of artificial intelligence (AI), AI-based bearing degradation prediction has aroused extensive attention. However, current methods rely on whole life cycle data, which is quite difficult to acquire in real industrial scenarios. To solve this problem, a rotor-bearing dynamic model is built to generate simulation signals for a range of spall sizes, and an improved domain adversarial neural network is proposed to transfer degradation knowledge from simulation data to experimental data. To be specific, complete simulation data is used to pre-train a network for learning comprehensive degradation knowledge, and guides the extracted high-level features in the adversarial domain adaptation stage to align with it as an additional optimization item. The proposed approach is verified on bearing degradation datasets under different working conditions, and results show that the proposed approach can successfully predict bearing degradation progress only with some early stage experimental data.

In the era of Industry 4.0, digital transformation has spurred the swift advancement of technologies, including intelligent predictive maintenance scheduling, prognostics and health management. The accurate prediction of remaining useful life plays a crucial role in these technologies as it extends power equipment’s safe operational duration and decreases the maintenance costs associated with unforeseen shutdowns. Also, the increased accessibility of data for monitoring system conditions has paved the way for the more immense adoption of machine learning models in prognostics and health management for power transformers. At the moment, with the ever-increasing demand for electricity, there is a corresponding increase in the degradation processes of power transformers. Transformers insulation system and more importantly, the paper insulation happens to be the principal part where the degradation is prominent. Therefore, an accurate prediction of the insulating paper condition through its degree of polymerization is required to guarantee the reliability of power transformers. In this regard, the predictions, reliability, and health monitoring of this power equipment can be actualized by modeling the degradation of transformer insulation paper through several machine learning frameworks. In this view, this review paper has been drafted not only to serve as a guide for researchers interested in the fields of transformer insulation system fault prognosis but also to offer insights into potential research directions as existing literature in modeling and evaluating transformer paper insulation is presented.

Data-driven methods have been shown to be suitable for the diagnosis and prognosis of the health of engineering systems. However, the training of data-driven methods usually requires a large amount of data, which is rarely available in industry. In addition, the prediction accuracy often degrades when operating or environmental conditions change or when there are similar systems with different technical characteristics. Transfer learning offers the possibility to transfer knowledge about the degradation behavior between such systems. However, there is a risk that the degradation behavior differs too much, which leads to a so-called negative transfer. Therefore, the authors argue that a similarity assessment of degradation behavior is essential. An assessment based on the operational data of systems seems particularly appropriate. In this paper, the suitability of time series similarity measures for such data-based similarity assessments is investigated. The current state of research is presented. Thereby, no studies on the similarity comparison of degradation curves of engineering systems using time series similarity measures are found. Furthermore, measures for assessing the similarity of degradation curves are identified and categorized. In a case study on filter clogging curves, similarity tests are performed using these measures to find the most similar time series. Various approaches are proposed for evaluation, two of which are used in this paper. The results show that mostly a good selection is made, with some measures performing particularly well. The work presented in this paper represents valuable groundwork for the use of time series similarity measures in transfer learning.

This paper explores nonlinear cumulative damage models applied to Prognostics and Health Management in electronic systems, with a focus on solder joint reliability under variable vibrations. Several cumulative damage models, including the total strain energy density model, are investigated to address the limitation of Miner’s rule. The State-of-Damage (SoD) concept is introduced as a pivotal metric for evaluating Prognostics and Health Management performance within each model. A novel SoD definition with a scale factor is proposed to balance linearity and exponential behavior, meeting the demands of real-time monitoring. Furthermore, by calculating Remaining Useful Life (RUL) using the SoD metric, the RUL predictions from Miner’s rule and total strain energy density model with the scale factor are compared. The results reveal that the total strain energy density model, enhanced with the scale factor, outperforms Miner’s rule in accurately predicting SoD and RUL. This study advances Prognostics and Health Management methodologies by introducing an innovative SoD concept with a scale factor, resulting in enhanced accuracy for both SoD and RUL predictions. This innovation holds the potential for more reliable electronic systems under variable loading conditions.

The zinc oxide (ZnO) arrester is positioned on the locomotive roof, preventing other onboard electrical equipment from being harmed by external and internal overvoltages. A number of locomotive arrester burnout and explosion accidents occur during traction supply network harmonic resonance, causing train stoppages and power outages in the network. This study first carries out high-frequency tests to investigate the wide frequency band electrical characteristics of the locomotive arrester and prove that the arrester is prone to fault during resonance. Consequently, a prognostics and health management (PHM) methodology for locomotive arresters is proposed, providing online monitoring, fault warning, and health assessment, which is beneficial to the operation and maintenance of arresters in the future.

Bearings are pivotal components of rotating machines where any defects could propagate and trigger systematic failures. Once faults are detected, accurately predicting remaining useful life (RUL) is essential for optimizing predictive maintenance. Although data-driven methods demonstrate promising performance in direct RUL prediction, their robustness and practicability need further improvement regarding physical interpretation and uncertainty quantification. This work leverages variational neural networks to model bearing degradation behind envelope spectra. A convolutional variational autoencoder for regression (CVAER) is developed to probabilistically predict RUL distributions with confidence measures. Enhanced average envelope spectra (AES) are used as network input for its physical robustness in bearing condition assessment and fault detection. The use of the envelope spectrum ensures that it contains only bearing-related information by removing other rotor-related frequencies, hence it improves the RUL prediction. Unlike traditional variational autoencoders, the probabilistic regressor and latent generator are formulated to quantify uncertainty in RUL estimates and learn meaningful latent representations conditioned on specific RUL. Experimental validations are conducted on vibration data collected using multiple accelerometers whose natural frequencies cover bearing resonance ranges to ensure fault detection reliability. Beyond conventional bearing diagnosis, envelope spectra are extended for statistical RUL prediction integrating physical knowledge of actual defect conditions. Comparative and ablation studies are conducted against benchmark models to demonstrate their effectiveness.

Hybrid electric vehicles (HEV) are a practical choice for energy saving in the transportation field. Degradation diagnosis (DD) is one of the main methods to guarantee system robustness. However, the classical DD methods cannot meet the requirements of HEV due to their system complexity. In this study, a novel Prognostics and Health Management (PHM) study was conducted to face these challenges. Firstly, a physical P2 HEV model with a rule-based controller was built, and its diesel engine sub-model was simplified by a neural network (NN) to ensure real-time performance of the degradation prognostics. Secondly, a degradation prognostics method based on gray relation analysis–principal component analysis (GRA-PCA) was illustrated, which could confirm degradation 2 s after the health index fell below the threshold. Finally, a degradation tolerance strategy based on long short term memory–model predictive control (LSTM-MPC) was performed to optimize vehicle speed tracing with minimal energy consumption and was validated by three cases. The result shows that the energy consumption stayed nearly unchanged for the engine degradation case. For the battery degradation case, the tracing error was reduced by 11.7% with 4.3% more energy consumption. For combined degradation, the strategy achieved a 12.3% tracing error reduction with 3.7% more energy consumption. The suggested PHM method guaranteed vehicle power performance under degradation situations.

Induction machines (IMs) are utilized in different industrial sectors such as manufacturing, transportation, transmission, and energy due to their ruggedness, low cost, and high efficiency. If IMs fail without advanced warning, unscheduled maintenance needs to be performed, leading to downtime and maintenance costs for asset owners. To avoid these, conducting prognostics and health management (PHM) for IMs is indispensable. There are different PHM methods (expert knowledge, physics-based, and machine learning) to analyze the health and estimate the remaining useful life (RUL) of IMs. It is essential to select appropriate methods and algorithms to solve practical engineering problems by comparing their pros and cons. This paper will systematically summarize the application of the PHM framework to IMs and comprehensively present how to select appropriate general methods as well as specific algorithms applied in the PHM for IMs to solve practical engineering problems, aiming to provide some guidance for future researchers and practitioners. [ABSTRACT FROM AUTHOR]

In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management systems to detect, identify, and forecast complex faults in a short time. Furthermore, it is necessary to ensure that these systems preserve their capabilities over time despite engine deterioration. This paper addresses these necessities by proposing an integrated system that considers the joint operation of feature extraction, anomaly detection, fault identification, and prognostic algorithms for engines with long operation times. To effectively reveal the actual engine condition, light adaptive degraded engine models are computed along with different health indicators that are used as inputs to train and test recognition and prediction models. The system is developed and evaluated using a specialized NASA platform which provides data from a turbofan engine fleet simultaneously experiencing long-term performance deterioration and faults. Contrary to other compared solutions, our results show that the proposed system is robust against the effects of engine deterioration, maintaining its level of detection, recognition, and prediction accuracy over a total engine service life. The low computational cost algorithms has generally fast performance in all stages, making the system suitable for online applications. [ABSTRACT FROM AUTHOR]

In industrial applications, rotating machines operate under real-time variable speed and load regimes. In the presence of faults, the degradation of critical components is accelerated significantly. Therefore, robust monitoring algorithms able to identify these faults become crucial. In the literature, it is hard to find comprehensive monitoring systems that include variable speed and load regimes with combined gearbox faults using electrical and vibration signals. For this purpose, a novel signal processing methodology including a geometric classification technique is proposed. This methodology is based on using different types of sensors such as current, voltage and vibration sensors with a regime normalization, which allows the grouping of different regimes belonging to the same health state. It consists of reducing dispersion between the class observations and separating other classes representing different health states including the variation in speed and load. Then, a peripheral threshold is proposed in our classifier to diagnose new health states. To verify the effectiveness of the methodology, current, voltage and vibration data from a gearbox system are collected under variable speed and load levels. [ABSTRACT FROM AUTHOR]

Over the past years, advanced prognostic models and approaches have been developed. Most existing approaches are tailored to one specific system and cannot adaptively be used on different systems. This can lead to years of research and expertise being put into implementing prognostic models without the capacity to predict system failures, either because of a lack of data or data quality or because failure behavior cannot be captured by data-driven models. In addition, prognostic models are often evaluated using metrics only related to the correctness of predictions, preventing meaningful evaluation of operational performance. This paper makes use of a framework that can automatically choose prognostic settings based on specific system data. It simultaneously optimizes the choice of methodologies using metrics that capture multiple aspects of prediction quality. We apply this framework to both a simulated data set and a real aircraft data set to characterize the impact of metrics on the choice of prognostic methodologies. The results show that the choice of optimization metric greatly impacts the output of the generic prognostic framework and the overall performance. In addition, a definition for data suitability is provided and assessed on the aircraft system data sets. [ABSTRACT FROM AUTHOR]

To meet the increasing demands for availability at reasonable cost, operators and maintainers of railway point machines are constantly looking for innovative techniques for switch condition monitoring and prediction. This includes automated fault root cause diagnosis based on measurement data (such as motor current curves) and other information. However, large, comprehensive sets of labeled data suitable for standard machine learning are not yet available. Existing data-driven approaches focus only on the differentiation of a few major fault categories at the level of the measurement data (i.e., the "fault symptoms"). There is great potential in hybrid models that use expert knowledge in combination with multiple sources of information to automatically identify failure causes at a much more detailed level. This paper discusses a Bayesian network diagnostic model for determining the root causes of faults in point machines, based on expert knowledge and few labeled data examples from the Netherlands. Human-interpretable current curve features and other information sources (e.g., past maintenance actions) are used as evidence. The result of the model is a ranking of the most likely failure causes with associated probabilities in terms of fuzzy multi-label classification, which is directly aimed at providing decision support to maintenance engineers. The validity and limitations of the model are demonstrated by a scenario-based evaluation and a brief analysis using information theoretic measures. We present the information sources used, the detailed development process and the analysis methodology. This article is intended to be a guide to developing similar models for various complex technical assets. [ABSTRACT FROM AUTHOR]

Integrating a model-based and data-driven approach to generate sufficient number of training data that include time-varying operating conditions is needed to develop a prognostics and health management technique using machine learning for a reusable rocket engine. A data-driven approach was used to predict the engine behavior, which cannot be determined using a model-based approach. This paper proposes a clustered regression model to predict various operating conditions for a reusable rocket engine. The training data were divided in advance according to the operating conditions using the Gaussian mixture model, and multiple regression models were individually trained on each cluster. By switching the regression models according to the operating conditions, the time-series data comprising various operating conditions, such as the combustion and chill-down phases and transition between phases, were accurately predicted when compared with the prediction results of a single regression model. The prediction results of the present hybrid approach agree reasonably well with the static firing test results of a reusable rocket engine if the explanatory variables are adequately included. By integrating model-based and data-driven approaches, adequate training data can be generated for prognostics and health management of the reusable liquid rocket engine. • This study improves the prediction accuracy of the data-driven approach. • Training data for the data-driven approach comprised varying operating conditions. • The varying operating conditions were classified using a Gaussian mixture model. • The regression models were trained based on each classified operating condition. • The prediction accuracy was improved using multiple regression models. [ABSTRACT FROM AUTHOR]

Because satellites cannot be repaired once launched, operators must detect anomalies early and prevent failures before they occur. Thus, satellite telemetry monitoring systems need to alert operators of anomalies and provide them with useful information to deal with these anomalies. However, traditional knowledge-based monitoring systems have the challenges of difficulty in building comprehensive models and a high dependency on experts. In recent years, data-driven approaches have been actively studied with the development of machine learning algorithms. These approaches solve the challenges of knowledge-based methods; however, they are often less capable of explaining anomalies than knowledge-based methods. In this study, we propose the new telemetry monitoring system with feature engineering to explain anomalies. The proposed method realizes identifiability of anomaly types and unusual telemetry by designing features based on moving averages, telemetry periods, waveform differences, and the Mahalanobis distance. We applied the proposed features to artificial and practical abnormal datasets and evaluated their usefulness. The results showed that the proposed method is capable of identifying trend, periodic, and waveform anomalies, specifying the telemetry in which the anomaly occurred and providing the information to operators.

Data-driven Prognostics and Health Management (PHM) become a crucial layer in the realm of predictive maintenance (PM). However, many industries develop PM technologies based on the monitoring of machine data to anticipate failures without considering the injected raw material. In reality, non-compliant material characteristics can affect the manufacturing tools leading to machine breakdowns and poor quality product. To cope with this situation, this paper proposes a new methodology that helps operators predicting machine breakdowns. In detail, the methodology starts by implementing an Extract, Transform, Load (ETL) process which aims to create a new and reliable dataset from heterogeneous sources. Then, a feature selection method is used for dimensionality reduction and keep only useful information. After that, the selected features are injected to Machine Learning (ML) algorithms to predict system breakdown occurrences. Finally, the novelty in this study, an auto-labeling algorithm based on material data and machine breakdown predictions is proposed. This algorithm aims to enhance raw material stock management, scheduling their consumption accordingly and thus reducing machine breakdowns. The developed methodology is applied to a real dataset of a French company, SCODER, that shows and pointed out promising perspectives in PM.

This study investigates a novel approach for assessing the health status of rotating machinery transmission systems by analyzing the dynamic degradation of bearings. The proposed method generates multi-dimensional data by creating virtual states and constructs a multi-dimensional model using virtual state-space in conjunction with mechanism model analysis. Innovatively, the Hammerstein–Wiener (HW) modeling technique from control theory is applied to identify these dynamic multi-dimensional models. The modeling experiments are performed, focusing on the model’s input and output types, the selection of nonlinear module estimators, the configuration of linear module transfer functions, and condition transfer. Dynamic degradation response signals are generated, and the method is validated using four widely recognized databases consisting of accurate measurement signals collected by vibration sensors. Experimental results demonstrated that the model achieved a modeling accuracy of 99% for multiple bearings under various conditions. The effectiveness of this dynamic modeling method is further confirmed through comparative experimental data and signal images. This approach offers a novel reference for evaluating the health status of transmission systems.

With the rapid development of science and technology, the integration and complexity of aerospace vehicles, weaponry, and large-scale chemical equipment are becoming higher and higher. PHM plays an important role in realizing reductions in equipment loss due to failures in many fields. In order to systematically sort through the research history of PHM and deeply analyze the development status of AR and DT technologies in the field of PHM, to clarify the current technical challenges and future development directions and to provide valuable references and insights for researchers, engineers, and decision-makers in the related fields, this paper summarizes the development of PHM in the engineering field. This paper summarizes the development of PHM in the field of engineering, from the initial PHM used in aerospace to the current PHM systems supported by various advanced technologies; analyzes the advantages and shortcomings of the digital twin and augmented reality technologies used for PHM; and organizes and summarizes the future directions and future research focuses of PHM research based on the existing technologies (mainly digital twins and augmented reality). After systematic research and study, we found that the integration of augmented reality and digital twin technologies will provide superb simulation capabilities and immersive operation and bring new challenges and opportunities. Therefore, it is also imperative to address the challenges and limitations that hinder the seamless integration of the new technologies.