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Accurate prediction of the remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs) is essential for maximizing their operational lifespan. However, existing methods often face limitations in two key areas: long-term prediction (beyond 168 h, or one week) and adaptability to varying operating conditions. To address these challenges, we propose a novel self-adaptive digital twin (SADT) model for RUL prediction of PEMFCs. Our approach uniquely integrates a deep convolutional neural network to generate robust health indicators (HIs) that maintain consistent monotonicity across diverse operating conditions. Additionally, we introduce a novel quantile Huber loss (QH-loss) function to enhance prediction accuracy and incorporate a transfer learning technique to improve adaptability under varying operational scenarios. Experimental results on PEMFC degradation datasets demonstrate that our method outperforms state-of-the-art techniques in long-term prediction accuracy, highlighting its potential to significantly extend fuel cell lifetimes. • A novel self-adaptive digital twin for predicting RUL of the PEMFC. • Transfer Learning enhanced RUL prediction accuracy and adaptability. • Universal monotonic HI applicable to various PEMFC operating conditions. [ABSTRACT FROM AUTHOR]

Accurately predicting the degradation and remaining useful life (RUL) of Proton Exchange Membrane Fuel Cells (PEMFCs) is crucial for enhancing their reliability, particularly in automotive and energy sectors. Traditional models often fail to capture the complex, non-linear degradation patterns of PEMFCs, resulting in suboptimal predictions. This study addresses these technological gaps by introducing a novel approach, the Self-Attention Temporal Dual Discriminator Generative Adversarial Network (SAT-DD-GAN). This model is designed to overcome the limitations of existing methods by integrating advanced Long Short-Term Memory (LSTM) networks for time-series analysis, a self-attention mechanism to focus on critical degradation signals, and dual discriminators to improve prediction accuracy and robustness. By addressing the gap in handling long-term dependencies and complex degradation patterns, the proposed SAT-DD-GAN bridges the disconnect between traditional models and the real-world degradation behavior of PEMFCs. The SAT-DD-GAN achieved groundbreaking predictive performance, with the lowest RMSE (0.983E-3 for static load, 1.012E-3 for dynamic load), MAPE (0.036E-1 and 0.253E-1), MAE (0.776E-3 and 0.806E-3), and the highest R2-score (0.9998 and 0.9983), alongside zero relative error for both datasets. These results demonstrate the model's ability to capture the intricate degradation dynamics more effectively than existing state-of-the-art methods. By filling the technology gap of accurately modeling PEMFC degradation under varying load conditions, this study establishes a new standard for PEMFC prognostics and paves the way for future innovations in optimizing fuel cell performance and longevity. • Introduced a novel SAT-DD-GAN model for accurate prediction of PEMFC stack voltage degradation and Remaining Useful Life (RUL). • Innovatively combined LSTM, self-attention, and dual discriminator mechanisms for enhanced predictive robustness. • Achieved superior accuracy, with RMSE as low as 0.000983 (static load) and R2-score of 0.9998 (static load). • Demonstrated advanced handling of static and dynamic loads, outperforming recent models in key prediction metrics. • Set a new benchmark in predictive maintenance for PEMFCs, offering potential advancements in neural network-driven prognostics. [ABSTRACT FROM AUTHOR]

Durability is one of the important factors limiting the large-scale commercial application of proton exchange membrane fuel cells (PEMFC) systems, and accurate lifetime prediction of the PEMFC is beneficial to enhance their durability and reliability. In this paper, a fusion prognostic model based on bidirectional temporal convolutional network, bidirectional gated recurrent unit, and extreme learning machine (BiTCN-BiGRU-ELM) is proposed to realize short-term degradation prediction and remaining useful life estimate of the PEMFC system. The nature-inspired metaheuristic of the crested porcupine optimizer algorithm is introduced to optimize the parameters of the proposed BiTCN-BiGRU-ELM method. The effectiveness of the hybrid method is verified under three current conditions: steady state, quasi-dynamic, and dynamic. The fusion model can significantly improve prediction performance compared to traditional machine learning methods. This method is of great significance for online lifetime prediction and health management of PEMFC. • A PEMFC degradation prediction model of BiTCN-BiGRU-ELM was proposed. • Improved prediction performance of ELM by BiTCN-BiGRU feature extraction. • Optimal model parameters were determined using a CPO algorithm. • The effect of different training lengths on prediction accuracy was investigated. • This fusion model can be applied to RUL's prediction under dynamic conditions. [ABSTRACT FROM AUTHOR]

To achieve the 8000-h proton exchange membrane fuel cell stack (PEM FCS) life target set by the U.S DoE and promote fuel cell hybrid electric vehicles (FCHEVs) massive introduction in the automotive market, using multi-fuel cell stack (MFCS) systems instead of single-fuel cell stack systems seems to be an interesting solution that deserves to be explored. MFCS systems' concept combines several small FCSs modules instead of using a single high-powered FCS module. The modularity in such systems can be exploited through energy management to improve their durability and extend their good energy-efficiency power range. However, FCSs' multiplicity makes it challenging to implement effective energy management strategies (EMSs). This paper proposes a remaining useful life (RUL) prognostic-based EMS to extend MFCS systems' lifetime while keeping their hydrogen consumption reasonable. For this purpose, a prognostic algorithm is developed to predict PEM FCSs' RUL in real-world automotive application scenarios. Then a rule-based EMS allocates the demand between stacks using prognostic results. The proposed strategy's performance is evaluated on a hybrid MFCS/battery system using Matlab/Simulink's environment. Simulation results show that implementing the proposed strategy instead of conventional EMSs can extend MFCS systems' lifetime by at least a factor of 2.35 while keeping their hydrogen consumption reasonable. © 2001 Elsevier Science. All rights reserved. • A post-prognostic decision-making strategy is proposed for MFCS systems. • RUL predictions are performed in real-world automotive scenarios. • The MFCS system's lifetime is significantly extended with the proposed EMS. • RUL is a suitable parameter for adaptive energy management decision-making. [ABSTRACT FROM AUTHOR]

Prediction of Remaining Useful Life (RUL) for Rolling Element Bearings (REB) has attracted widespread attention from academia and industry. However, there are still several bottlenecks, including the effective utilization of multi-sensor data, the interpretability of prediction models, and the prediction across the entire life cycle, which limit prediction accuracy. In view of that, we propose a knowledge-based explainable life-cycle RUL prediction framework. First, considering the feature fusion of fast-changing signals, the Pearson correlation coefficient matrix and feature transformation objective function are incorporated to an Improved Graph Convolutional Autoencoder. Furthermore, to integrate the multi-source signals, a Cascaded Multi-head Self-attention Autoencoder with Characteristic Guidance is proposed to construct health indicators. Then, the whole life cycle of REB is divided into different stages based on the Continuous Gradient Recognition with Outlier Detection. With the development of Measurement-based Correction Life Formula and Bidirectional Recursive Gated Dual Attention Unit, accurate life-cycle RUL prediction is achieved. Data from self-designed test rig and PHM 2012 Prognostic challenge datasets are analyzed with the proposed framework and five existing prediction models. Compared with the strongest prediction model among the five, the proposed framework demonstrates significant improvements. For the data from self-designed test rig, there is a 1.66 % enhancement in Corrected Cumulative Relative Accuracy (CCRA) and a 49.00 % improvement in Coefficient of Determination (R2). For the PHM 2012 datasets, there is a 4.04 % increase in CCRA and a 120.72 % boost in R2. [Display omitted] • Advocate explainable knowledge-data integration for RUL prediction throughout the life cycle, leveraging multi-sensor data. • Introduce a novel unsupervised learning framework that integrates IGCA and CMSACG to construct HI. • A two-stage prediction framework, fusing MCLF and BR-GDAU, is proposed for predicting the life cycle RUL. • The effectiveness of the proposed framework is validated using both Self-Designed Experiment-Derived and PHM 2012 Prognostic Challenge Bearing Datasets. [ABSTRACT FROM AUTHOR]

The durability is a key factor of fuel cell in large-scale application of automobiles. It is great important to real the lifetime and health status of fuel cells, especially in vehicle driving conditions. Based on the experimental data, this paper used the model-based method and data-driven method to predict the life of fuel cells, respectively. The model-based method established the equivalent circuit model according to the mapping law of electrochemical impedance spectroscopy (EIS) and the parameters of the equivalent circuit model were identified by newly considering the phenomenon of voltage recovery, revealing the strong linear correlation between impedance and voltage. The data-driven method selected a combinatorial optimization PSO-LSSVM algorithm and compared it with the prediction results of MATLAB's time series toolbox. The results show the mean square error (MSE), End of Life (EOL) of the model-based method, the time series toolbox and PSO-LSSVM algorithm are 0.019, 8.2 × 10−3 and 4.42 × 10−5, the 260h, 438h and 427h, respectively. Comparing with the real EOL of 421h, the error of PSO-LSSVM algorithm is only 6 h, which means the remaining useful life can be predicted with high accurately. Thus this study provides a reference for fault identification and life prediction of fuel cell health management. • The equivalent circuit model is established to realize the segmented voltage prediction and analysis of fuel cells. • The MSE of combinatorial optimized algorithms PSO-LSSVM to predict voltage is only 4.42 × 10−5. • The method in paper provides a reference for fault identification and life prediction of PEMFC's health management. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cells (PEMFCs) offer high energy conversion rate and low pollution, while low durability of PEMFCs hinder the further development. Accurately predicting degradation trends and remaining useful life (RUL) are crucial to timely detecting problem and improving PEMFCs durability. In this paper, an enhanced transfer learning (Add-TL) based on adder network (AdderNet) and deep domain confusion is proposed for the prediction of PEMFCs. To reinforce the ability of extracting key features from extensive data, AdderNet with attention mechanism is applied. Furthermore, the fused deep domain confusion employs existing data to achieve higher prediction accuracy. The experimental results demonstrate that Add-TL outperforms other classical methods in predicting degradation trend, the optimal adjusted R-square (R2) values for the two datasets are 0.99879 and 0.99906, respectively. When Add-TL is applied to RUL prediction, the accuracy scores for two datasets reach 0.995, which further validating the applicability of Add-TL in PEMFCs prediction. • AdderNet applied for the first time to predict degradation trends and RUL of PEMFCs. • The method proposed in this paper integrates deep learning and transfer learning. • The incorporation of transfer learning the connections between data, aiming to improve the accuracy of RUL prediction. • Add-TL predicted static and dynamic conditions, providing a comprehensive validation. • Add-TL achieves best performance when training on datasets with varying lengths. [ABSTRACT FROM AUTHOR]

This paper proposed a prediction algorithm for the degraded actuator taking into account the impact of estimation error of hidden index in the closed-loop system. To this end, a unified prediction framework is established to evaluate the hidden degradation information and recursively update the degradation model parameters simultaneously. The advantage is that the prediction framework can comprehensively compensate the estimation error of hidden degradation index caused by system uncertainty. To jointly estimate the degradation information in avoidance of the impact of system uncertainty, a modified adaptive Kalman filter is designed, and the proof of stability is provided. With the priori estimate from the filter, the degradation model parameters are updated by the inverse filtering probability based on Bayes' theorem. It is followed by the computation of the remaining useful life (RUL) prediction utilizing aforementioned hidden degradation information and the latest degradation model. The effectiveness of the proposed RUL prediction algorithm is demonstrated by the degraded actuator in the continuous casting process. • A novel prediction framework is designed for degraded actuator in closed-loop system. • Adaptive degradation model is established by modified adaptive Kalman filter. • The estimation error of hidden degradation index is integrated. • The stability of optimized adaptive Kalman filter is proven. • Recursive prediction algorithm is performed without full life data. [ABSTRACT FROM AUTHOR]

Schedule maintenance activities for hydrogen fuel cell vehicles are aimed to keep them in better condition in long-term operation. In this paper, we develop a reinforcement learning based schedule maintenance strategy, which is used to comprehensively consider the balance between safety and maintenance cost, so as to find the optimal maintenance strategy. A multi-level framework is established with remaining useful life of key components, fault tree analysis, and logistics cost model to generate an exploration environment that can express the operational stability, the failure rate of components, and the cost of repair and storage. The trained agent combines a deep neural network to explore the optimal strategy under the dynamic reliability of key components. Finite steps are the constraints. Safety rates, maintenance cost, and episode of operation are incorporated into a multi-objective reward function. The hydrogen supply circuit of fuel cell vehicle is simulated via Python. The trained agent is compared with traditional time-based schedule maintenance strategy and corrective maintenance strategy. The results show that the reinforcement learning based schedule maintenance agent has optimized the total reward, cost control and accident rate by 77%, 59% and 5%, respectively, compared with the traditional time-based schedule maintenance strategy. In the safe operation management of fuel cell vehicles, efficient and stable decision-making ability has been achieved. • An adaptive maintenance strategy of deep reinforcement learning is proposed. • An exploration environment of FCV is established. • The deep Q network is proposed to solve the maintenance problem. • The efficient and stable decision-making ability is much better than time-based scheduled maintenance strategy. [ABSTRACT FROM AUTHOR]

Predicting the Remaining Useful Life of a machine components or systems is a pivotal technology in condition-based maintenance and essential for ensuring the reliability and safety in various production-engineering applications. The influx of extensive industrial data has notably enhanced the efficacy of data-driven Remaining Useful Life prediction models, especially deep learning models. One of the promising deep learning model architectures is the Transformer-based model with the Self-Attention mechanism at its core. However, inherent limitation arise when applying Self-Attention to high- frequency time-series data with large window sizes. Due to its high computational complexity, hardware limitations hinder the practical implementation of Transformer with Self-Attention in production-engineering applications. This study looks into the utilization of alternative Attention modules with reduced complexity, making it more applicable to high-frequency time-series data. In order to allow comparability, this study uses the well-known C-MPASS dataset for benchmarking Remaining Useful Life approaches. Although this dataset does not consist of high-frequency data, it demonstrates the usefulness of alternative Attention modules without noteworthy losses in model accuracy. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) is one of the critical factors for proton exchange membrane fuel cells (PEMFCs), as it is hindering the commercialization of PEMFCs in various fields. In this research, a novel deep learning (DL) algorithm, i.e. the Non-stationary Transformer is newly applied to the RUL prediction of a PEMFC stack. For better prediction accuracy, the discrete wavelet transform (DWT) is utilized to denoise the original data before the normalization process. Prediction results of the proposed Non-stationary Transformer-based PEMFC RUL prediction method are analyzed under different lengths of training datasets, under different time steps ahead prediction, and for different PEMFC End of Life (EoL) threshold values and compared to those of other DL-based prediction methods including the long short-term memory (LSTM)-based and echo state network (ESN)-based methods. Results show that the proposed method outperforms the LSTM-based and ESN-based methods for all different cases studied in this research. • The Non-stationary Transformer is newly applied to PEMFC RUL prediction method. • The DWT method is utilized to denoise original data to accurate the prediction. • The proposed RUL prediction method is compared to LSTM and ESN-based methods. • RUL prediction methods are analyzed under various dataset lengths and time steps. • The proposed RUL prediction method outperforms LSTM and ESN-based in accuracy. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction and degradation assessment are pivotal components of prognostic and health management (PHM) and represent vital tasks in the implementation of predictive maintenance for bearings. In recent years, data-driven PHM techniques for bearings have made substantial progress through the integration of deep learning methods. However, modeling the temporal dependencies inherent in raw vibration signals for both degradation assessment and RUL prediction remains a significant challenge. Hence, we propose a joint optimization architecture that uses a temporal convolutional auto-encoder (TCAE) for the degradation assessment and RUL prediction of bearings. Specifically, the architecture includes a sequence-to-sequence model to extract degradation-sensitive features from the raw signal and utilizes temporal distribution characterization (TDC) and a nonlinear regressor to determine the degradation stages and predict RUL, respectively. Our framework integrates the tasks of degradation assessment and RUL prediction in a unified, end-to-end manner, using raw signals as input, resulting in high RUL prediction accuracy (RMSE = 0.0832) on publicly available and self-built datasets. Our approach outperforms state-of-the-art methods, indicating its potential to significantly advance the field of PHM for bearings. • A novel temporal convolutional auto-encoder (TCAE) is proposed. • TCAE jointly optimizes remaining useful life prediction and degradation assessment. • Degradation-sensitive features are extracted with joint supervised and unsupervised learning. • Experimental case studies verified validity and superiority. [ABSTRACT FROM AUTHOR]

In order to realize the remaining useful life (RUL) prediction of mechanical equipment under different operating conditions, a domain adaption residual separable convolutional neural network (DRSCN) model is proposed in this paper. In the DRSCN model, instead of the traditional convolutional layer, a residual separable convolutional module is developed to improve the feature extraction ability of the model. Moreover, a multi-kernel maximum mean discrepancy metric function and an adversarial learning mechanism are embedded in the DRSCN model to enhance its ability to resist domain shifts, thus improving the cross-domain RUL prediction accuracy of the model. The effectiveness of the DRSCN model is verified on an aircraft engine dataset. The experimental results show that the proposed model can realize high-accuracy RUL prediction. [Display omitted] • A DRSCN model is proposed for cross-domain RUL prediction. • MMD is embedded in the DRSCN model to enhance its generalization performance. • Adversarial learning mechanism is adopted to realize domain adaption of the model. • A residual separable convolution module is proposed to replace convolutional layer. [ABSTRACT FROM AUTHOR]

We introduce a general path Gamma-Gamma model for degradation measures, related to different inspection times functions, obtaining flexible forms of degradation paths. One important contribution of the proposed model is the way the degradation rate is modeled. It is composed of two random components: one random effect quantifying the specific features of each device and a dynamic effect, common to all devices, measuring the impact of the environment on the degradation. The model is identifiable under mild constraints. Besides producing gains regarding the interpretability of the parameters, this decomposition generates a parsimonious model, reducing computational time. The relation between degradation and failure time is obtained, allowing a computational approximation for the failure time distribution. The model performance is evaluated through simulation, helping to guide the prior specifications to model identification. The proposed model is applied to analyze fatigue crack growth data. We compare the proposed model with the traditional linear Weibull model and with a dynamic linear Normal model. Results show that the proposed methodology is competitive in predicting failure times and estimating the remaining useful life. • We develop a dynamic general path Gamma-Gamma model for positive degradation measurements. • The degradation rate is a function of unit-specific and environmental effects. • The environmental effects evolve over time considering a Markovian equation. • Different functions relate the degradation measures and inspection times. • Analysis of simulated and true datasets pointed out that it is a competitive model. [ABSTRACT FROM AUTHOR]

This paper focuses on the implementation of predictive maintenance processes using both physics-based and data-driven methods, specifically applied to a Salvagnini panel bender. The primary objective is to enhance production data analysis through digital twin predictions, enabling the calculation of process parameters that cannot be directly measured. Two key aspects are addressed in this study: Firstly, two global measures of the effective cumulative load on a machine during its lifetime are formulated. These measures can be used as criteria for estimating the health condition of a machine and for comparing different machines. To achieve this, dimensionless parameters are derived from actual production data. Secondly, a remaining useful life (RUL) model is developed for two machine components relying on production data analysis and digital twin predictions. The first component under consideration is a hybrid hydraulic actuator. Internal leakage prediction for this component is performed based on its data history, resulting in a corresponding trend curve. Additionally, wear of critical bearings is calculated using an analytical model that depends on the production history log. Through the proposed hybrid approach, this paper aims to enhance the predictive maintenance process by leveraging both physics-based and data-driven methodologies. The findings from this study can offer valuable insights for optimizing maintenance strategies and improving the overall efficiency of similar manufacturing systems. [ABSTRACT FROM AUTHOR]

Within the paradigm of industry 5.0, manufacturing systems are seeking for human-centred production, where the operator finds high-level supervision tasks. In this context, low-level decision making should be performed by machines themselves. In this paper, a hybrid prognosis algorithm is developed to automatically inspect the cutting edges of drill-bits and to predict their Remaining Useful Life (RUL) and the associated probability density function. The solution relies on the automatic measurement of flank wear through convolutional filtering and edge detection. Prognosis exploits particle filter, which updates multi-layer perceptron with online data, to adaptively predict drill-bits RUL. The solution reduces the experimental preliminary run-to-failures needed for training standard machine learning algorithms, exploiting them in a real-time adaptive scenario, while predicting tool RUL under untested and variable cutting process operations. The algorithm uses direct wear observations, taken during set-up times (e.g., tool changes, workpiece change), thus not interfering with the process. [ABSTRACT FROM AUTHOR]

In recent years, predictive maintenance tasks, especially for bearings, have become increasingly important. Solutions for these use cases concentrate on the classification of faults and the estimation of the Remaining Useful Life (RUL). As of today, these solutions suffer from a lack of training samples. In addition, these solutions often require high-frequency accelerometers, incurring significant costs. To overcome these challenges, this research proposes a combined classification and RUL estimation solution based on a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. This solution relies on a hybrid feature extraction approach, making it especially appropriate for low-cost accelerometers with low sampling frequencies. In addition, it uses transfer learning to be suitable for applications with only a few training samples. [ABSTRACT FROM AUTHOR]

Hydrogen fuel has shown promise as a clean, alternative fuel aiding in the reduction of fossil fuel dependence within the transportation sector. However, hydrogen refueling stations and infrastructure remains a barrier and are a prerequisite for consumer adoption of low-cost and low-emission fuel cell electric vehicles (FCEVs). The costs for FCEV fueling include both station capital costs and operation and maintenance (O&M) costs. Contributing to these O&M costs, unscheduled maintenance is presently more costly and more frequent than for similar gasoline fueling infrastructure and is asserted to be a limiting factor in achieving FCEV customer acceptance and cost parity. Unscheduled maintenance leads to longer station downtime, therefore, causing an increase in missed fueling opportunities, which forces customers to seek refueling at other operable stations that may be significantly farther away. This research proposes a framework for a hydrogen station prognostics health monitoring (H2S PHM) model that can minimize unexpected downtime by predicting the remaining useful life for primary hydrogen station components within the major station subsystems. The H2S PHM model is a data-driven statistical model, based on O&M data collected from 34 retail hydrogen stations located in the U.S. The primary subcomponents studied are the dispenser, compressor, and chiller. The remaining useful life calculations are used to decide whether or not maintenance should be completed based on the prediction and expected future station use. This paper presents the background, method, and results for the H2S PHM model as for a means for improving station availability and customer confidence in FCEVs and hydrogen infrastructure. • Hydrogen station reliability is a key contributor to market success. • Mean fills between failure and station availability are lower than conventional technologies. • A prognostics health monitoring system can improve hydrogen station availability. • Estimated remaining useful life of primary components assists in maintenance decisions. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) can ensure the safety and reliability of power batteries during operation, reduce the failure rate and operating costs, and enhance user experience. However, battery degradation is a complex, nonlinear dynamic process that is difficult to fully comprehend and predicting RUL remains a significant challenge. To address this issue, the hybrid data-driven prediction model PCA-CNN-BiLSTM was proposed in this paper, which combines principal component analysis (PCA), convolutional neural network (CNN), and bi-directional long short-term memory (Bi-LSTM) network. PCA was applied to downscale and whiten the health factor (HF) to maximize the extraction of important features of lifespan decay, while reducing the correlation between features. The convolution kernel of the CNN was used to explore the local region feature information of the input information and search for the common patterns among the neighboring data. Additionally, the model parameters and computational efforts were reduced through pooling. Finally, battery RUL prediction was achieved using Bi-LSTM, which has the advantages of effectively enhancing model accuracy and reducing the risk of over-fitting by taking into account both past and future data. The performance of the proposed model was evaluated utilizing NASA and CALCE's battery datasets, and the results suggest that it exhibits a high level of accuracy across various datasets. Compared to other methods, the PCA-CNN-BiLSTM method has the best performance indicators for predicting battery RUL, including RMSE, MAE, MAPE, RUL e and DOL. This indicates that the proposed model has better fitting performance, accuracy, robustness, and generalization ability. [Display omitted] • Propose a novel battery RUL prediction method based on hybrid data-driven model. • Nineteen health factors were extracted, including segmented discharge time and temperature. • The effectiveness of the PCA-CNN-BiLSTM was verified on multiple public datasets. [ABSTRACT FROM AUTHOR]

A robust model based on stochastic processes with Copula function is developed for remaining useful life (RUL) prediction of subsea pipeline subject to corrosion-fatigue degradation. Gamma-based stochastic degradation process is used to simulate the corrosion degradation of pipeline, and fatigue crack propagation is estimated with Wiener process. The Particle Filter (PF) and Kalman Filter (KF) are utilized to update the model parameters. Then, an improved joint distribution model incorporating marginal distributions and copula function is presented to capture the complex dependencies between corrosion and fatigue. By establishing an acceptable pipeline failure threshold, a comparison is conducted among three stochastic process models to assess the practicality and effectiveness of the developed robust model. The results indicate that copula-based model has superiority in predictive accuracy with a deviation of 0.5. Considering interaction of corrosion-fatigue has a substantial impact on improving the accuracy of RUL. The present model can significantly contribute to the proactive maintenance and management of subsea pipelines, thereby enhancing operational efficiency and reducing potential risks. [ABSTRACT FROM AUTHOR]

• A new bearing prognosis framework is built upon the time-domain signal preprocessing and physics-based wavelet neural network (WNN). • Empirical mode decomposition (EMD) and statistical time series analysis are adopted as performance multipliers of WNN. • The framework enables the physical interpretation of bearing fault diagnosis and yields excellent predictive performance. • Comprehensive case studies using multiple datasets demonstrate the feasibility of the framework. Rolling bearings are important components in rotating machinery in various industries. Conducting intelligent prognostics for bearing remaining useful life (RUL) prediction plays an important role in the health management and reliability assessment of these machinery systems. While the bearing fault prognosis using measured system response through machine learning techniques has attracted significant attention and demonstrated promising potential, completely data-driven approaches face some challenges in understanding complex domains with data efficient learning. In this research, we develop an integral bearing fault prognosis framework informed by the well-designed time-domain signal preprocessing to conduct the bearing RUL prediction. This framework is built upon physical feature-oriented signal preprocessing and an associated wavelet neural network (WNN). Sequential procedures of time-domain analyses are proposed to extract the physical features of bearing degradation. Empirical mode decomposition (EMD) is specifically chosen owing to its capability of handling bearing fault signals that are nonstationary with underlying nonlinearities. The WNN is a new class of neural networks that combines the classic neural network and wavelet analysis. Here in this research a WNN model built upon B-spline mother wavelet to suit the preceding EMD-based signal preprocessing is constructed to process the bearing degradation features extracted and then identify their correlation with bearing RUL. The combination of EMD-based signal preprocessing and B-spline mother wavelet in WNN enables the network to learn the input-output correlation in a physical sense. Case studies formulated upon multiple bearing datasets and multiple benchmark methods are carried out to systematically validate the proposed framework. The results consistently demonstrate prediction accuracy and performance robustness. [ABSTRACT FROM AUTHOR]

• Physics-Constrained Bayesian Neural Network. • Failure prognostics of lithium-ion batteries. • Comparative study of physics-based, data-driven, and physics-constrained models. In order to predict the remaining useful life (RUL) of lithium-ion batteries, a capacity degradation model may be developed using either simplified physical laws or machine learning-based methods. It is observed that even though degradation models based on simplified physical laws are easy to implement, they may result in large error in the application of failure prognostics. While data-driven prognostics models can provide more accurate degradation forecasting, they may require a large volume of training data and may invoke predictions inconsistent with physical laws. It is also very challenging for existing methods to predict the RUL at the early stages of battery life. In this paper, we propose a Bayesian physics-constrained neural network for battery RUL prediction by overcoming limitations of the current methods. In the proposed method, a neural differential operator is learned from the first 100 cycles of data. The neural differential operator is modeled with a Bayesian neural network architecture that separates the fixed history dependence from the time dependence to isolate epistemic uncertainty quantification. Using the battery dataset presented in the paper by Severson et al. as an example, we compare our proposed method with a simplified physics-based degradation forecasting model and two data-driven prognostics models. The results show that the proposed physics-constrained neural network can provide more accurate RUL estimation than the other methods with the same group of training data. Most importantly, the proposed method allows for RUL prediction at earlier stages of the battery life cycle. [ABSTRACT FROM AUTHOR]

Accurately evaluating the remaining useful life (RUL) of aircraft engines is crucial for ensuring operational safety and reliability, and serves as a critical foundation for making informed maintenance decisions. In this paper, a novel prediction framework is proposed for forecasting the RUL of engines, which utilizes a dual-frequency enhanced attention network architecture built upon separable convolutional neural networks. First, the information volume criterion (IVC) index and information content threshold (CIT) equation are designed, which are applied to quantitatively quantify the degradation features of the sensor and remove redundant information. In addition, this paper introduces two trainable frequency-enhanced modules, namely the Fourier transform module (FMB-f) and the wavelet transform module (FMB-w), to incorporate physical rules information into the prediction framework, dynamically capture the global trend and local details of the degradation index, and further improve the prediction performance and robustness of the prediction model. Furthermore, the proposed efficient channel attention block generates a unique set of weights for each possible vector sample, which establishes the interdependence among different sensors, thereby augmenting the prediction stability and precision of the framework. The experimental demonstrate that the proposed RUL prediction framework can deliver accurate RUL predictions. • The IVC and CIT are used to quantify and eliminate degradation information. • A dual-frequency block(FEB-f and FEB-w) with trainable parameters is proposed. • FEB-w and FEB-f jointly trained with SDFEPN for dynamic coordination. • The integration of physical rules into the SDFEPN enhances the interpretability. [ABSTRACT FROM AUTHOR]

Estimating the remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs) is key to predicting and increasing their durability. This paper investigates six deep learning techniques – long short-term memory (LSTM) networks, gated recurrent units (GRU), 1-D convolutional neural network (CNN)-LSTMs, 1D–CNN–GRUs, 1D–CNN–Bidirectional-LSTMs, and 1D–CNN–Bidirectional-GRUs – for RUL and long-term degradation trend prediction for a five-cell PEMFC stack operated with and without current ripples. The hyperparameters of all models are optimized for minimum root-mean-square error (RMSE) of the stack voltage using tree-structured Parzen estimators (TPE) with hyperband pruning. The obtained prediction results are benchmarked using the coefficient of determination (R2), RMSE, and mean absolute percentage error (MAPE) in stack voltage predictions and relative error (RE) in RUL estimates. The comparative analysis for models trained on 50% datasets reveals that the best R2, RMSE, MAPE, and RE of 98.87E-2, 1.258E-3, 0.285E-1, and 0.0425E-1 are offered by the LSTM model for the stack without current ripples. Further, for the stack with current ripples, the best R2, RMSE, MAPE, and RE of 99.482E-2, 1.77E-3, 0.444E-1, and 0.089E-1 are exhibited by the 1D–CNN–Bidirectional-GRU model. The LSTM model also reveals excellent transfer learning performance on the stack with current ripples with the best R2, RMSE, MAPE, and RE of 98.91E-2, 2.455E-3, 0.362E-1, and 0.044E-1, respectively. [Display omitted] • Six deep learning models are developed for PEMFC RUL and degradation prediction. • Hyperparameters of each model are optimized using TPE for static and dynamic loading. • Static loading models exhibited excellent transfer learning performance. • All tuned models performed superior to recent reported approaches in RMSE. • LSTM and 1D–CNN–Bidirectional-GRU demonstrated best results trained on 50% data. [ABSTRACT FROM AUTHOR]

A good weight initialization is crucial to accelerate the convergence of the weights in a neural network. However, training a neural network is still time-consuming, despite recent advances in weight initialization approaches. In this paper, we propose a mathematical framework for the weight initialization in the last layer of a neural network. We first derive analytically a tight constraint on the weights that accelerates the convergence of the weights during the back-propagation algorithm. We then use linear regression and Lagrange multipliers to analytically derive the optimal initial weights and initial bias of the last layer, that minimize the initial training loss given the derived tight constraint. We also show that the restrictive assumption of traditional weight initialization algorithms that the expected value of the weights is zero is redundant for our approach. We first apply our proposed weight initialization approach to a Convolutional Neural Network that predicts the Remaining Useful Life of aircraft engines. The initial training and validation loss are relatively small, the weights do not get stuck in a local optimum, and the convergence of the weights is accelerated. We compare our approach with several benchmark strategies. Compared to the best performing state-of-the-art initialization strategy (Kaiming initialization), our approach needs 34% less epochs to reach the same validation loss. We also apply our approach to ResNets for the CIFAR-100 dataset, combined with transfer learning. Here, the initial accuracy is already at least 53%. This gives a faster weight convergence and a higher test accuracy than the benchmark strategies. [ABSTRACT FROM AUTHOR]

In order to achieve a fast and accurate prediction for remaining useful life (RUL) of proton exchange membrane fuel cells (PEMFCs), an improved degradation model named RCLMA is developed which integrates the residual convolutional blocks, the long short-term memory (LSTM) units and the multi-head self-attention layers. Firstly, the stack voltages of two PEMFCs are collected, reconstructed and smoothed. Secondly, the RCLMA model is constructed and validated based on the experimental data, and the effects of input size and output size on the prediction performance are studied. Finally, ablation experiments are conducted to investigate the role of each part of the RCLMA model in the prediction. The results show that the RCLMA model reaches the best prediction performance with a root mean square error (RMSE) of 0.01785 and a S c o r e R U L of 0.994589 when the input size is 200 and the output size is 30, and the prediction time does not exceed 5 s. All three parts of the RCLMA model make important contributions to the prediction performance, where the residual part is partial to finding the overall trend of the data, the recurrent part is inclined to obtain the time dependence and the self-attention part plays a good capture of both. [Display omitted] • Effects of input size and output size on prediction performance of RCLMA model are studied. • Ablation experiments are conducted to investigate the role of each part in prediction. • The best prediction performance is achieved with an RMSE of 0.01785 and a score RUL of 0.994589. • The self-attention part plays a good capture of both the overall trend and time dependence. [ABSTRACT FROM AUTHOR]

In recent years, fuel cells have attracted more and more attention in the energy field. However, accurate evaluation of durability is one of the key aspects of large-scale commercial application of fuel cells. The data-driven method has good fitting performance for predicting remaining useful life (RUL) at complex and nonlinear systems. In this paper, Cycle Reservoir with Jump with multi-inputs and multi-outputs (MIMO-CRJ) is proposed to further improve the fitting speed and reduce the complexity of the system which improved the structure of echo state network (ESN). Different from the previous method that only uses voltage to predict the voltage, this paper considers stack current, temperature and pressure, reactant flow rate in fuel cell operation as input variables, and extracts more degradation information to predict the voltage trend. Experimental results show that MIMO-CRJ has better performance than Cycle Reservoir with Jump with single -inputs and single-output (SISO-CRJ) and ESN networks under both static and quasi-dynamic current conditions. • The multi-input Cycle Reservoir with Jump network is proposed for prognostics. • The proposed model can be used for remaining useful life on the static current and dynamic current experiments. • The MIMO-CRJ is used for short-term prediction and long-term prediction of PEMFC. [ABSTRACT FROM AUTHOR]

A robust recursive zonotopic set-membership approach for remaining useful life forecasting with application to linear parameter-varying systems is proposed in this paper. The proposed approach addresses systems with degraded components formulated as a system-level prognostics problem. Thus, the critical degraded components of the system are augmented to the states resulting a nonlinear system that is reformulated as a linear parameter-varying model. Hence, joint estimation of states and parameters is adopted in a zonotopic set-membership scheme with an optimal linear matrix inequality-based tuning and assuming unknown-but-bounded noises and uncertainties. As a result, a recursive zonotopic set-membership approach is proposed for remaining useful life forecasting based on the prediction of the failure precursors of degraded systems. Finally, this approach is tested on a DC–DC converter case study with unknown degradation behaviors, and the obtained results show the estimation and the forecasting accuracy of this methodology. • Zonotopic set-membership for prognostics applications. • Joint estimation of states and parameters for degraded systems. • Bounded degradation prediction and remaining useful life forecasting. • LMI-based optimization for robust zonotopic observer tuning for LPV systems. [ABSTRACT FROM AUTHOR]

• A prognosis framework of reaction wheel motor in space satellites is proposed. • Real motor degradation data of 3 years accelerated-life tests are utilized. • System-level prognostics is employed to account the effects of component degradation. • Failure threshold is imposed on the system according to the design requirement. The reaction wheels actuated by motors are widely used for advanced attitude control of satellites. During the satellite operation, the performance of reaction wheel motor degrades and results in unexpected failures. To guarantee the reliability and safety of satellites, it is important to predict its remaining useful life while it is in operation. To address this issue, this study presents a system-level prognostics approach for the reaction wheel motor, by regarding it as a system composed of multiple components. The approach is demonstrated by using the motor operation data obtained during the accelerated-life tests on ground for 3 years. Health degradation of each components of the motor are estimated using the adaptive extended Kalman filter. Failure threshold of the motor performance is established by the design requirement on characteristic curve. The anomaly detection and failure prediction are performed using the shifting kernel particle filter. [ABSTRACT FROM AUTHOR]

A fuel cell is a power generation device that directly converts chemical energy into electrical energy through chemical reactions; fuel cells are widely used in aerospace, electric vehicle, and small-scale stationary engine applications. The complex phenomena including mass/heat transfer, electrochemical reactions, and ion/electron conduction, can significantly affect the energy efficiency and durability of fuel cells, but are difficult to determine completely. Machine learning (ML) performs well in solving complex problems in engineering applications and scientific research. In this paper, a systematic review is conducted to explore ML methods, including traditional ML and deep learning (DL) methods, applied to fuel cells for performance evaluation (material selection, chemical reaction modeling, and polarization curves), durability prediction (state of health, fault diagnostics, and remaining useful life), and application monitoring. Then comparisons of traditional ML and DL methods are discussed, while the similarities and differences between ML and integrated physics simulations are also concluded. Eventually, the scope of ML methods applied to fuel cells is presented, and outlooks of future researches on ML applications in fuel cells are identified. • A systematical review is conducted to study on tradition ML and DL methods applied to fuel cells. • Applications of ML in performance evaluation, durability prediction and application monitoring of fuel cells are analyzed. • The comparisons of tradition ML and DL methods are investigated. • Similarities and differences between ML and that integrated physics simulation are discussed. • Scope of ML methods applied to fuel cells for local and global problems is presented. [ABSTRACT FROM AUTHOR]

The remaining useful life prediction is significant for Lithium-ion batteries to ensure safety and reliability. Due to the advantages of handling time sequence data, recurrent neural network based methods have achieved impressive performance on RUL prediction. However, most of these methods develop the RUL prediction model without considering the operating time of the battery, which is an important factor on capacity degradation. Therefore, this paper proposed a Temporal Transformer Network (TTN) for RUL of Lithium-ion batteries. The proposed method combines the self-attention mechanism of the Transformer Network with Denoising Autoencoder to implement the noise of raw data. More importantly, the proposed method designs a temporal encoding layer to introduce the operating time to the input of RUL prediction model. The performance of the proposed method is evaluated on two frequently used battery datasets. The proposed method achieves the best result compared with other frequently used RUL prediction methods. [ABSTRACT FROM AUTHOR]

The search for sustainable but environmentally friendly medium of harnessing energy for the automotive industry has led to the evolution of various energy generating and converting devices. One of such energy converting device is fuel cells. Despite the merits associated to the performance of proton exchange membrane (PEM) fuel cells, issues relating to the cost and remaining useful life prediction still persist hence impeding their further commercialization especially in the automotive industry. In spite of the progress made by the research community in developing various predictive models in order to mitigate these challenges, the accuracy of these developed models has lately become active research direction. The current study explored the accuracy of recurrent neural network, bi recurrent neural network, combined convolutional neural network and bi recurrent neural network in predicting the remaining useful life of a PEM fuel cell. The presence of the convolutional neural network was mainly to ensure pre – processing of the bi recurrent neural network for the extraction of high level features. To reduce the possibility of overfitting, a dropout approach coupled with callback technique is adopted. Validation of the model was executed based on an experimental data. The outcome of the investigation highlighted the key role of the convolutional neural network in improving the accuracy of the recurrent neural network. Comparing the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the present model with other models, the developed model yielded the least values indicating a higher accuracy. For instance, the relative error showed a least value of 0.12 for the combined convolutional neural network and bi recurrent neural network compared to the long short term memory with 2.61 reported in previous studies. • Evaluation of various existing predictive models for PEM fuel cells is studied. • Convolutional neural network accuracy in predicting the rate of degradation for PEM fuel cell is captured. • The developed predictive model have higher accuracy compared to other models. • Future studies should explore further combination of various predictive models and the effect on the accuracy. [ABSTRACT FROM AUTHOR]

Remaining useful life prediction is of huge significance in preventing equipment malfunctions and reducing maintenance costs. Currently, machine learning algorithms have become hotspots in remaining useful life prediction due to their high flexibility and convenience. However, machine learnings require large amounts of data, and their prediction performance depends heavily on the selection of hyper-parameters. To overcome these shortcomings, a novel remaining useful life prediction method for small sample cases is proposed based on multi-support vector regression fusion. In the offline training phase, the fusion model is established, consisting of multiple support vector regression sub-models To obtain the optimal sub-model parameters, the Bayesian optimization algorithm is applied and an improved optimization target is formulated with various metrics describing regression and prediction performance. In the online prediction phase, an adaptive weight updating algorithm based on dynamic time warping is developed to measure the fitness of each sub-model and determine the corresponding weight value. The C-MAPSS engine dataset is used to test the performance of the proposed method, along with some existing machine learning methods as comparison. The proposed method only requires 30% of the training data sample to achieve high accuracy, with a root mean square error of 14.98, which is superior to other state-of-the-art methods. The results demonstrate the superiority of the proposed method. • A multi-support vector regression fusion model is proposed for remaining useful life prediction. • The Bayesian optimization algorithm with improved optimization target is applied for parameter selection. • An adaptive weight updated algorithm is proposed to measure model fitness. • The proposed method can achieve high accuracy with fewer data sample. • The effectiveness is validated by aero-engine dataset. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries (LIBs) degradation can be classified into capacity reduction and increase of the internal resistance. Accurate estimation of degradation is important to determine the economic value and the remaining useful life of the battery. This paper adopts dynamic electrochemical impedance spectroscopy (EIS) to evaluate degradation while charging the battery, without requiring additional time for the analysis. A new method is proposed to remove the artifacts arising from the time-variance of the system during the measurement. The validity of the method is confirmed by comparing it with the results obtained by static EIS measurements at equilibrium. The spectra are fitted to an equivalent circuit containing three resistances (R 0 , R 1 , R 2). The method is applied to data obtained before and after aging cycles performed at 1.4C charge and 2.0C discharge rates, at 25 °C, 10 °C and −5 °C. The resistance of the films (R 1), which is obtained from the semicircle at high frequencies in the EIS spectrum, has proved to be the key indicator of aging, and linearly depends on the SoC. The increase of R 1 dominates among all the resistances, particularly at higher temperatures. Dynamic EIS is considered a powerful tool to diagnose LIBs in practical applications. [Display omitted] • Analysis of degradation based on dynamic electrochemical impedance spectroscopy. • The proposed model corrects the artifact caused by the variation of SoC while measuring. • The method is applied on cells aged with fast charging at 25 °C, 10 °C and −5 °C. • Films and charge transfer resistances dominate the battery aging at higher temperatures. • The resistance associated with the films, R 1 , is a key indicator of degradation and SoC. [ABSTRACT FROM AUTHOR]

Accurate remaining useful life (RUL) prediction for lithium-ion batteries (LIBs) is important for proper equipment operation. Among the numerous existing studies on battery research, data-driven approaches have gained significant attention because they obviate the need for complex chemical and physical modeling of battery processes. However, in most current data-driven methods, the sliding window size values are determined empirically, which affects both the time required for model prediction and the prediction accuracy. To address this issue, this paper proposes a two-stage prediction method for battery capacity aging trajectories. In the first stage, a false nearest neighbor (FNN) technique is utilized to infer the sliding window size of the battery, reducing the error associated with manually selecting the sliding window size. In the second stage, a novel triple parallel attention network (TPANet) based on convolutional neural networks (CNNs) and attention units is developed, which extracts the input battery capacity degradation features through a parallel mechanism. The introduction of attention units in each parallel branch allows the model to enhance the capture of capacity regeneration phenomena as well as long-term dependence on input data. The proposed approach is validated on two publicly available datasets as well as a battery developed by ourselves. Diverse simulation results demonstrate the ability of the proposed approach to accurately predict the RUL of LIBs in a short time with broad generalization capabilities. • A FNN technique is utilized to provide a sliding window size reference value for battery capacity data. • A novel TPANet approach is utilized to predict battery capacity data. • Multiple datasets verify that the proposed method has the advantages of high prediction accuracy, short consumption time of the algorithm, and wide generalization. [ABSTRACT FROM AUTHOR]

• The depth, relative distance, angle of cracks and other factors are comprehensively considered and implanted into the established three-dimensional FE models to innovatively investigate the crack coupling effect. • Cylindrical coordinate system is employed so that all factors can be converted into digital form and fed into the following GA-BP prediction model to predict SIFs. • The combined SIFs are integration into a prognostic model for RUL prediction. The propagation of fatigue cracks can be influenced by the crack coupling effect. Previous studies qualitatively focused on observing simulation data to determine the influence patterns of crack parameters on stress intensity factors (SIFs). In this study, investigating the crack coupling effect innovatively, the simulation data is integrated into a neural network and SIFs are predicted over time, enabling the remaining useful life of pipelines with coupling effect can be predicted systematically. Specifically, by establishing a finite element model with coupling cracks, the comprehensive effects of crack depth, as well as relative position and growth angle based on cylindrical coordinates, on the crack coupling effect were investigated. After obtaining the simulation data, a genetic algorithm-back propagation (GA-BP) neural network is constructed to predict the SIFs. Finally, by integrating the SIF prediction model with Paris' law, a method for predicting cracks growth and pipeline remaining useful life (RUL) considering the coupling effect of coupling cracks in complex relative positions was proposed. To ensure the accuracy of the predictions, the differences between the predictive results of this study and API 579 in predicting the SIF of coupling cracks were compared. The proposed method quantitatively predicts the cracks coupling effect and improves the system of the RUL prediction under the comprehensive influence of multiple cracks, thus to predict pipeline health more realistically and lay the foundation for maintenance. [Display omitted] [ABSTRACT FROM AUTHOR]

• The degradation of tensile and fatigue properties of high-speed train bearing after long-term service were explored. • The decline rates of fatigue strength in bearing steel after long-term service are 20.0% and 41.2%, respectively. • The mean length of secondary cracks increases from 8.3 μm to 9.8 μm, which indicating that more cracks initiated in used bearing. • It would provide a new insight for the mechanical property evolution law in high-speed train bearing. The remaining useful life prediction of the bearings after long-term service is a crucial issue. Exploring the fatigue failure mechanism of bearing is helpful to grasp the dynamic evolution law during its whole period. In this paper, the tensile and fatigue property degradation law of bearings for high-speed train under different service mileages were explored. The results display that there is a downward trend for the tensile/fatigue strength and elongation of the bearing as prolonging its service mileage. The area proportion of dimples declines while that of cleavage plane increases in bearing steel after long-term service. The variations of secondary crack length and equivalent diameters of cleavage plane indicate that more cracks initiated in damaged bearing under cyclic loading. Based on the energy principle, the fatigue failure mechanism of bearings after long-term service was elaborated with the plastic deformation, crack initiation and growth resistance. The results would provide theoretical reference for the fatigue property degradation law of high-speed train bearings. [ABSTRACT FROM AUTHOR]

The battery management system (BMS) plays a critical role in electric vehicles (EVs), with the prediction of remaining useful life (RUL) being of utmost importance. This functionality enables real-time monitoring and enhances driver assistance through intelligent driving mechanisms. To improve the precision of stack voltage degradation and RUL prognostication for proton exchange membrane fuel cell (PEMFC) aging, especially with limited real-time training data, we propose a novel approach using a layer-enhanced quantum long short-term memory model with transfer learning. The proposed model is trained offline using historical stack voltage degradation data from a source stack, which is then fine-tuned and transferred to a target stack. Comparative evaluations demonstrate the model's robustness, with consistently low root mean square errors in stack voltage predictions, even with increased training data. The proposed model significantly improves RUL prediction accuracy, achieving a maximum relative accuracy enhancement of 1.33 % when initiating predictions at 712 h. These results affirm the effectiveness of our approach in enhancing the accuracy of PEMFC aging prognosis, thereby offering valuable insights for practical integration within the BMS framework for EVs. • A novel prognostic model is used to predict the RUL of PEMFC stacks. • An embedding layer-based quantum LSTM is presented with transfer learning. • The power of quantum computing assists model training with accelerated network convergence. • Online RUL prediction yields an absolute error of 0 %. [ABSTRACT FROM AUTHOR]

The decision-making process concerning the maintenance of equipment significantly impacts the safety and reliability of the aeroengine system. A cyber-aeroengine system (CAS) integrates intricate aeroengine components with a cyber decision-making space based on the communication network, thereby facilitating computational support for forecasting the health status of the aeroengine system. Within the cyber realm, we introduce a novel approach termed the graph spatio-temporal attention recurrent network with phase-lag index (PLI-GSTARN) to assess the degradation states of aeroengine systems. This method constructs a spatio-temporal graph based on sensor signal correlations and information reachability across distributed sensors within the physical equipment. Subsequently, the proposed framework employs a graph attention network to capture spatial features and a long short-term memory (LSTM) layer equipped with an attention mechanism to capture temporal features inherent in the constructed graph. Ultimately, this methodology is applied to cyber–physical systems within aircraft engines to manage remaining health life and diagnose faults, yielding commendable outcomes. • The cyber-aeroengine system (CAS) integrates complex aeroengine components with a cyber decision-making space facilitated by communication networks. This integration enhances computational support for predicting the health status of aeroengine systems. • Introduces the novel approach of graph spatio-temporal attention recurrent network with phase-lag index (PLI-GSTARN). This method constructs a spatio-temporal graph using sensor signal correlations across distributed sensors, enabling advanced assessment of aeroengine degradation states. • Combines a graph attention network for spatial features and an LSTM layer with attention mechanisms for temporal features. Applied to aircraft engine cyber-physical systems, this approach effectively manages remaining health life and diagnoses faults, showing significant operational benefits. [ABSTRACT FROM AUTHOR]

This review presents an approach to traditional and advanced modeling, simulation, and analysis techniques in the context of thermal barrier coatings (TBCs), augmented by AI technology. The aim is to comprehensively comprehend TBC behavior, optimize designs, and facilitate proactive maintenance strategies. The discussed methods include thermal conductivity modeling, thermal stress simulation, crack propagation, and mechanical behavior analysis in TBC systems, employing diverse approaches such as effective medium theories, molecular dynamics simulations, computational fluid dynamics, multiscale modeling, and finite element analysis. Notably, the review underscores the burgeoning application of AI in TBC simulations, exploring various machine learning algorithms such as support vector machines, random forests, neural networks, deep learning methodologies, and data-driven modeling techniques. The findings posit that AI harbors significant potential to revolutionize TBCs across multiple domains, encompassing material and structural design, performance optimization and prediction, monitoring, prognostics and health management, and the enhancement of remaining useful life predictions. [ABSTRACT FROM AUTHOR]

The problem of the Remaining Useful Life (RUL) prediction, aiming at providing an accurate estimate of the remaining time from the current predicting moment to the complete failure of the device, has gained significant attention from researchers in recent years. In this paper, to overcome the shortcomings of rigid combination for temporal and spatial features in most existing RUL prediction approaches, a spatial–temporal homogeneous feature extractor, named Dual-Mixer model, is firstly proposed. Flexible layer-wise progressive feature fusion is employed to ensure the homogeneity of spatial–temporal features and enhance the prediction accuracy. Secondly, the Feature Space Global Relationship Invariance (FSGRI) training method is introduced based on supervised contrastive learning. This method maintains the consistency of relationships between sample features with their degradation process during model training, simplifying the subsequently regression task in the output layer and improving the model's performance in RUL prediction. Finally, the effectiveness of the proposed method is validated through comparisons with other latest research works on the C-MAPSS dataset. The Dual-Mixer model demonstrates superiority across most metrics, while the FSGRI training method shows an average improvement of 7.00% and 2.41% in RMSE and MAPE, respectively, for all baseline models. Our experiments and model code are publicly available at https://github.com/fuen1590/PhmDeepLearningProjects. [ABSTRACT FROM AUTHOR]

The issue of uncertainty in Remaining Useful Life (RUL) prediction based on the Deep Learning (DL) framework has gained increased attention in recent years. The probabilistic distribution is usually the effective way to capture the associated uncertainty in RUL prediction. However, most existing research relies on a single distribution that assumes a singular underlying pattern in the data. This type of approach restricts the ability to describe dynamic industrial processes and lacks robustness in adapting to the complexity of time-varying degradation. This paper proposes an approach to improve uncertainty quantification in RUL prediction based on the Multi-Distribution Fusion (MDF) structure. Initially, multiple possible RUL prediction results are produced. Subsequently, the MDF is used to integrate the former results with different weights and outputs the final RUL prediction distribution. The proposed method excels in uncertainty capturing in complex scenarios and provides a deeper understanding of the underlying dynamics of the monitoring data. The application of MDF resulted in more enhanced and robust uncertainty concerning RUL predictions. To validate the effectiveness of the proposed method, two neural networks, the Long Short-Term Memory (LSTM) network and Convolutional Neural Network (CNN), are individually combined with MDF and applied to the C-MAPSS datasets. • The concept of multi-distribution is introduced to quantify the aleatoric uncertainty. • The proposed structure enhances the performance of RUL prediction with uncertainty quantification. • The proposed framework is designed for general deep learning methods, such as CNN and RNN. [ABSTRACT FROM AUTHOR]

• Proposed a novel framework for predicting lubricant degradation in rolling bearings. • TVTF signal processing is designed to extract changes in vibration responses. • Proposed feature extraction with harmonic, sideband components in bearing vibrations. • Designed the transformer-based HarFT model to effectively predict RUL. • Framework validated on 10 bearings, shows superior prediction performance. Accurate prediction of the remaining useful life (RUL) of lubricants in rolling bearings is crucial for maintaining efficient operation of rotating machinery and ensuring timely lubricant replacement. We propose a comprehensive framework that integrates the temporal variation transfer function (TVTF), harmonic-sideband Matrix, and the harmonic frequency transformer (HarFT), a transformer-based model. This approach effectively utilizes vibration characteristics to enhance the accuracy of lubricant degradation prediction in rolling bearings. Validation with rolling bearings experiencing lubrication failure confirms that our framework significantly outperforms alternative methods in RUL prediction. The proposed framework excels in extracting and analyzing harmonic components from vibration responses, enabling detection of minute status variations due to lubricant degradation. By applying explainable artificial intelligence (XAI), it is possible to ascertain the rationale behind the RUL predicted by the HarFT model, facilitating evidence-based decisions. Our research provides a novel strategy for lubricant RUL assessment in rolling bearings, thereby improving reliability and maintenance efficiency in industrial applications. [ABSTRACT FROM AUTHOR]

Developing reliable health indicators for industrial assets is essential for accurate condition monitoring, fault detection, and predicting the remaining useful lifetime. However, constructing such indicators is challenging, especially given the increasing complexity of industrial systems and the not well-understood degradation dynamics. Previously proposed autoencoder-based methods for unsupervised health indicator construction faced the difficulty of constraining the latent representation over the system's lifetime to obtain trendable and prognosable health indicators. Koopman operator theory provides a natural solution to this challenge. In this work, we first demonstrate the successful application of the Deep Koopman Operator approach for learning the dynamics of industrial systems. This results in a latent representation that provides sufficient information for estimating the remaining useful life of the asset. Secondly, we propose a novel Koopman-Inspired Degradation Model for modelling the degradation of dynamical systems with control. The Koopman-based algorithms demonstrate superior or comparable performance with autoencoder-based approaches in predicting the remaining useful life of assets such as CNC milling machine cutters and Li-ion batteries. • Koopman operator theory allows to learn the linear dynamics in the observables space. • Deep Koopman Operator (DKO) can be applied to model dynamics of industrial systems. • Koopman-Inspired Degradation Model (KIDM) allows for modelling of control systems. • The learned latent representations can be utilized as health indicators. [ABSTRACT FROM AUTHOR]

• The peak-of-Histogram (PoHG) is extracted as the new health indicator. • Multi-task learning dictionaries are constructed based on the evolution of PoHG. • A multi-task learning mixture density network (MTL-MDN) method is proposed. • The MTL-MDN method accomplishes the uncertainty assessment of RUL. Existing remaining useful life (RUL) predictions of rolling element bearings have the following shortcomings. 1) Model-driven methods typically employ a sole model for processing the data of an individual, making it challenging to accommodate the variety of degradation behaviors and susceptible to abnormal interference. 2) Data-driven methods place greater emphasis on training data, and in reality, it can be challenging to acquire comprehensive data covering the lifecycle. 3) Many studies fail to give adequate attention to the assessment of RUL uncertainty. This paper proposes a multi-task learning mixture density network (MTL-MDN) method to address these issues. Firstly, the peak-of-Histogram (PoHG) is extracted and served as the novel health indicators. Secondly, multi-task learning dictionaries are constructed based on the evolution law of PoHG, thus combining both model-driven and data-driven strategies. Finally, a multi-task learning strategy is proposed with mixture density networks. It effectively accomplishes the collaborative learning objective of numerous degradation samples in the regression problem and accomplishes the uncertainty assessment of RUL. After analyzing the experimental and real-world degradation data of rolling element bearings throughout their lifecycle, and comparing it to other modern RUL prediction methods, it becomes evident that the proposed MTL-MDN method offers superior prediction accuracy and robustness. [ABSTRACT FROM AUTHOR]

The deterioration of engineering systems might reduce the system reliability and prompt maintenance operations that may disrupt the ability of the systems to provide regular service. Estimating the Remaining Useful Life (RUL) of the system requires an understanding of the deterioration processes acting on it. Recent formulations have shifted the focus of deterioration modeling from the system as a whole to the individual, time-varying state-variables that define the characteristics of the system. These state-dependent formulations depend on the selected models for the evolution of the state-variables over time. However, most available models rely on simplifying assumptions that disregard the true nature of the processes, either by discretizing the time domain or by assuming independence among the several processes acting on the system. This paper proposes using a system of Stochastic Differential Equations (SDEs) to model the state variables' evolution. The proposed formulation captures the continuous nature of the processes and accounts for the possible interactions among them. In addition, results from stochastic calculus can be used to facilitate the simulation of the processes and to obtain closed-form solutions for the distribution of the state variables over time and the RUL of the system. Moreover, the proposed SDEs can be calibrated based on observations of the state variables, should those be obtained via testing/monitoring or simulations. A procedure for calibration is introduced, and several numerical examples are investigated to highlight the different scenarios encountered in practice. Finally, the proposed SDE formulation is used to predict the RUL of lithium-ion batteries using actual Structural Health Monitoring data. [ABSTRACT FROM AUTHOR]

Generative adversarial networks (GANs) have recently attracted attention owing to their impressive ability in generating high-quality and novel synthetic datasets such as signals, images, audio, text and animations that represent actual sample characteristics. To date, many GANs practical applications have been successfully implemented in industrial scenarios, however, no work regarding GANs has been reported for focusing on prognostic and health management (PHM) in industrial applications. This paper delivers a state-of-the-art review and presents the recent advances of GANs and continuous-variant GANs, including architectures, pros and cons of GANs in terms of theoretical perspective and PHM. Meanwhile, the commonly used GANs for PHM applications, including data collection/transmission/storage, fault diagnostics, remaining useful life (RUL) estimation and prognostics of degenerate trajectories, decision making as well as maintenance, are reviewed and investigated comprehensively. Eventually, some potential challenges, opportunities and possible research directions of GANs for future promising applications in PHM are suggested and highlighted. [ABSTRACT FROM AUTHOR]

Degradation characteristics of lithium-ion battery pack system (LIBPs) cannot be well described directly by the existing life model of cell, such as the interference imposed by stochastic uncertainty and coupling effect of multiple cells. In this article, we devise a battery capacity estimation and prediction algorithm leveraging deep learning and least squares regression, and develop a stochastic model of capacity degradation obeying a lognormal distribution and a reliability evaluation methodology to reveal the internal evolutionary rules and health status of LIBPs with multi-state logic. We generate a comprehensive dataset consisting of 150 cells and 3 battery packs derived from the cloud platform for cross-validation and take real-world vehicle daily operating charging and discharging data as an input for the proposed framework. The validation results indicate that our best model achieves 0.33 % mean absolute percentage error and 0.44 % root mean squared percent error for state of health estimation and 0.317 mean absolute error in remaining lifetime prediction. Further, remaining capacity probability distribution curves are established with estimated and predicted results, and reliability fade tendencies are meticulously profiled based on the series-parallel structure, which enables accurate electric vehicle service time evaluation under different conditions of uncertainty factors introduced by battery inconsistency and randomness of driving behaviors. This work highlights the promise of deep learning to supplant resource- and time-consuming traditional approaches, and emphasizes the potential of employing reliability theory to rapidly design new-generation battery safety management frameworks. • A multi-channel separated hybrid neural network model aiming for achieving better accuracy and improved generalization. • Using only segments of the daily charging and discharging data with 30s sampling interval are capable of modelling and predicting the evolution of nonlinear battery systems. • Based on SOH estimation and RUL prediction at the cell level and reliability analysis at the pack level, the service time and safety status evaluation throughout the life cycle of electric vehicles is realized. • Premeditating the goal of building an excellent battery system, the impact of the series-parallel structure on the reliability of battery packs is investigated. [ABSTRACT FROM AUTHOR]

As a sparse data analysis method, a grey model faces challenges in interpretability for its effective application in uncertain systems. This study proposes a generalized grey model (GGM) based on symbolic regression, designed to improve the intelligence and adaptability of grey models. The GGM serves as a unified framework, integrating various grey model families and addresses regression challenges to determine the model structure. Symbolic regression in the GGM identifies symbolic input-output relationships, offering an interpretable approach for structure determination. By leveraging the non-uniqueness principle in grey system theory and employing structural penalty parameters, the model balances complexity and interpretability. A comparative analysis between GGM and conventional grey function models is conducted focusing on the differences in modeling, structure identification, and parameter optimization. Validation on the M3 competition dataset demonstrated the GGM's superior performance, achieving a significant reduction in prediction error compared to other grey forecasting models. Additionally, a rigorous analysis of aircraft lifespan data underscored the robustness and accuracy of GGM in practical engineering applications. • An interpretable generalized grey model framework is constructed. • The symbolic regression algorithm is used to identify the structure of grey model. • The generalized grey model can strike a balance between complexity and interpretability. [ABSTRACT FROM AUTHOR]