Search

Machining tools are vital components of intelligent manufacturing systems whose state and remaining useful life (RUL) determine product quality. Specifically, the wearing of tool reduces its capability of production yield and diminishes product quality. Therefore, a capability-based RUL prediction approach is proposed in this paper to thoroughly evaluate the state and RUL of machining tools. First, the connotation of tool capability is discussed, and a framework for quality assurance capability-based RUL prediction is proposed. Product quality, which can be used to assess the capability of tool, is modelled and expanded to consider non-geometric dimensioning and tolerancing (non-GD&T) features based on the classic geometric dimensioning and tolerancing (GD&T) system. Second, a physics-based model of process is developed to estimate the non-GD&T features and calculate tool wear. Third, a hybrid data-driven and physics-based model is developed to quantitatively assess the capability of tool based on the comprehensive quality estimation. Finally, a case study of rolling machining tool is carried out to verify the effectiveness and proactiveness of the proposed framework, and the final result highlights its rationality and accuracy in estimating the RUL of machining tools with better interpretation. [ABSTRACT FROM AUTHOR]

Accurately predicting the capacity and remaining useful life (RUL) of lithium-ion batteries is crucial for their reliable and safe functioning. In this research, we propose a dual Siamese transformer-encoder-based network consisting of two subnetworks to improve the RUL prediction capability and for wider applications. The first subnetwork, autoTrans, adopts a transformer-encoder architecture to form an autoencoder structure for the feature extraction. The second subnetwork, regTrans, is a transformer-encoder-based regressor model which takes the featuring encodings from autoTrans as inputs and makes the RUL prediction. To enhance the model's robustness, both subnetworks employ the Siamese architecture for handling raw and noisy inputs. A joint training strategy is applied on autoTrans and regTrans to optimize the proposed approach. The experimental verification is conducted on the NASA battery, and our model achieves the best average results across different evaluation criteria. Furthermore, we also apply our model to the turbofan engine dataset and demonstrate promising performance as well. [ABSTRACT FROM AUTHOR]

Current maintenance models lack the technological capabilities to generate key performance indicators that optimize both critical equipment behavior and the surrounding processes. Artificial intelligence offers powerful tools for predicting and interpreting sensor data collected from such equipment, enabling continuous improvement. This paper proposes a tool that leverages deep learning to predict the remaining useful life (RUL) of a large-scale mining crusher. Additionally, the model incorporates result interpretation algorithms to analyze both training cycles and subsequent production cycles. This analysis not only identifies a process "fingerprint" but also recommends adjustments to the crusher system within the ongoing maintenance plan. By employing a dense neural network and interpretation algorithms, the proposed tool predicts the current crusher cycle's RUL and compares its interpretation graphs to the process fingerprint. This comparison identifies discrepancies, which in turn inform maintenance recommendations tailored to specific crusher components. [ABSTRACT FROM AUTHOR]

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]

A digital twin-driven lifecycle management method is proposed to monitor the lifespan of motorized spindle. The lifecycle management system is designed and developed based on the combined programming of MATLAB, ANSYS, and LabVIEW. The prediction models of lifespan are built according to the digital twin (DT) data of thermal characteristics of the motorized spindle. The DT for thermal characteristics is realized through correcting the thermal boundary conditions according to the correction models which are established according to the measured and simulated temperatures at thermal key points. The temperature domain and threshold models of the cooling channel are constructed using the exponential fitting method to monitor the working status of the cooling system. The experimental results show that the DT-driven lifecycle management system can effectively monitor the status and remaining useful life of the motorized spindle, which provides a basis for the lifecycle management of the motorized spindle. [ABSTRACT FROM AUTHOR]

In conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment. [ABSTRACT FROM AUTHOR]

The prediction of remaining useful life (RUL) is a critical component of prognostic and health management for industrial systems. In recent decades, there has been a surge of interest in RUL prediction based on degradation data of a well‐defined degradation index (DI). However, in many real‐world applications, the DI may not be readily available and must be constructed from complex source data, rendering many existing methods inapplicable. Motivated by multivariate sensor data from industrial induction motors, this paper proposes a novel prognostic framework that develops a nonlinear DI, serving as an ensemble of representative features, and employs a similarity‐based method for RUL prediction. The proposed framework enables online prediction of RUL by dynamically updating information from the in‐service unit. Simulation studies and a case study on three‐phase industrial induction motors demonstrate that the proposed framework can effectively extract reliability information from various channels and predict RUL with high accuracy. [ABSTRACT FROM AUTHOR]

This document is a summary of a special issue of the journal Quality & Reliability Engineering International. The issue showcases original research presented at the 2023 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering. The research papers cover a diverse range of topics related to reliability and safety in modern engineering systems. The papers address areas such as reliability modeling and analysis, reliability-based design and optimization, failure/safety analysis and prevention, and maintainability. The authors express their appreciation to the journal and the reviewers for their support and hope that the research presented will be valuable to researchers, engineers, scientists, and students in the field. [Extracted from the article]

Estimation of fault severity throughout bearing life is required for bearing prognostics and remaining useful life estimation. The useful life limit varies based on the function and criticality of the bearing and the machine. Most research has focused on the initial stages when faults are small. This paper presents novel severity categories of a spall-like fault located on a deep groove bearing outer race by investigating the dynamic behavior. Different stages of fault severity, classified as small, medium, and large faults, are characterized from fault initiation to faults extending to angles corresponding to several balls interacting simultaneously with the fault. This research combines dynamic modeling and experiments. The dynamic model is used to understand dynamic behavior, and the experiments are used to validate the results. The dynamic behavior is used to analyze and explain new unique patterns in the vibration signatures of different fault severities. This study enhances the capabilities for estimating fault severity in ball bearings, providing valuable insights that contribute to the development of more accurate and effective maintenance strategies. This is particularly relevant in scenarios where the system can operate efficiently even with large faults exceeding the "small" category. [ABSTRACT FROM AUTHOR]

The ability to accurately predict the remaining useful life (RUL) of lithium-ion batteries is critical for the health management of electronic gadgets and electric cars, among other things. This research proposes a novel deep learning model called frequency-domain attention mixture of expert (FBAME) to increase RUL prediction accuracy. The model first creates a frequency domain attention transformer to process the raw data from a frequency domain perspective, then employs BiLSTM and multihead attention mechanism to extract the lithium battery capacity degradation features, followed by further mixing and screening of the essential features via DualMLP, and finally employs the mixer expert to combine the predicted results. The proposed model's performance is examined using battery datasets from NASA and CALCE, and the findings demonstrate that our proposed FBAME model can significantly enhance the RUL prediction accuracy for lithium-ion batteries. The relative errors are 0.011 and 0.002, the maximum average absolute errors do not surpass 0.0330 and 0.0234, and the maximum average root mean square errors do not exceed 0.0422 and 0.0309. [ABSTRACT FROM AUTHOR]

AbstractRemaining useful life (RUL) prediction is an essential tool for enhancing the reliability and resilience of systems. Most existing RUL prediction methods consider degradation-induced failures, and assume there exists a fixed failure threshold for degradation signals. In many real applications, however, it is difficult or even impossible to find such failure thresholds, making the RUL prediction a challenging task. This study addresses the challenge by exploiting the method of forward intensity, a novel tool introduced in econometrics. In particular, the historical degradation signals are treated as time-varying covariates for the forward intensity. The dynamics of unobserved future degradation signals are captured by a time-varying regression parameter in the forward intensity function, which are usually estimated using the maximum pseudolikelihood method in existing studies. To improve prediction accuracy, this study proposes a new estimation method based on smoothing splines. The proposed method not only allows RUL prediction in the absence of a fixed failure threshold, but also achieves satisfactory prediction accuracy with reasonable interpretability of covariate effects. Prediction intervals for the RUL are also developed. The effectiveness of the proposed method is validated through Monte Carlo simulations and three real-data examples. The results show that the proposed method outperforms existing methods in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Accurate prediction of remaining useful life (RUL) for in‐service systems plays an important role in ensuring efficient operation of industrial equipment and in preventing unexpected equipment failures. In this paper, we present a prognostic framework for real‐time RUL prediction based on joint modeling of both degradation signals and time‐to‐event data. The proposed model employs a change point‐based general path model to capture signal non‐linearity and Neural network (NN) based Cox model to link the time‐to‐event data with the estimated degradation trend. An empirical two‐step scheme for hyperparameter estimation is proposed to enhance prognostic accuracy. Furthermore, an efficient Bayesian model updating procedure, integrated with recursive particle filtering, is used to facilitate online prediction, achieving accurate RUL prediction in real‐time and accounting for uncertainties in RUL prediction. Simulation and real‐life case studies demonstrate the advantages of the proposed method over existing approaches. [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]

Remaining useful life (RUL) is a key indicator for assessing the health status of lithium (Li)-ion batteries, and realizing accurate and reliable RUL prediction is crucial for the proper operation of battery systems. As high-capacity Li batteries have more complex chemical properties, most of the current RUL prediction methods rely mainly on a priori knowledge to make judgments. As a result, prediction accuracy is not high. In this study, we developed a health indicator-capacity (HI-C) dual Gaussian process regression (GPR) model based on incremental capacity analysis (ICA) and optimized its kernel function to achieve accurate RUL prediction for 280 Ah high-capacity Li batteries. Validation against the United States National Aeronautics and Space Administration's four battery test datasets showed that the use of the HI-C dual GPR model resulted in a mean absolute percentage error and root mean square error of less than 0.02 and 0.04, respectively, for the four battery-rated capacity predictions. Additionally, this model achieved an absolute error of less than five battery failure turns. Compared with a single model, the HI-C dual GPR model not only had high accuracy but also solved the problem that the HI was not measurable in the actual battery operation, which made it more suitable for RUL prediction of Li batteries. [ABSTRACT FROM AUTHOR]

Estimating the reliability and maintainability (R & M) parameters is crucial in various industrial applications. It serves purposes such as evaluating system performance and safety, minimising the risk and cost of potential failures, and designing efficient maintenance strategies. This task becomes challenging for complex repairable systems, where failures can occur due to different causes and performance may be affected by various covariates (such as material, environment, and labour). Another challenge in R & M studies arises from the presence of censorship in failure times. Existing methodologies often fail to account for all the aforementioned aspects of system‐related data in R & M analysis. By incorporating valuable information from covariates and utilising data from censored failure times alongside complete failure data, the accuracy of R & M parameter estimation can be significantly improved. This paper develops reliability models for repairable systems with multiple failure causes in the presence of covariates. The system can also be subject to imperfect maintenance. The R & M parameters are then estimated by applying the Kijima Type I and II model's virtual age concept. The proposed technique is illustrated using two case studies on gas pipelines and aero‐engine systems. Through these case studies, we show that the proposed method not only provides more efficient estimates of the R & M parameters compared to the alternative approach, but it is also easier to apply and yields more straightforward interpretations. [ABSTRACT FROM AUTHOR]

It is difficult to effectively predict remaining useful life (RUL) due to limited training samples and lack of life labels in some operating conditions of practical engineering. When existing deep learning methods predict the RUL of equipment in such operating conditions using a model trained on other operating conditions, the poor generalization of the model caused by large distribution differences cannot be ignored. In this study, an RUL prediction method based on integrated dilated convolution transfer is proposed. This method jointly adjusts the model parameters by inverting the loss function of the RUL prediction module and the domain adaptive module, and then realizes the extraction of domain‐invariant features between different operating condition data through the feature extraction module, which provides support for transfer RUL prediction between different operating conditions. In the feature extraction module, a one‐dimensional convolution network with a large‐size kernel reduces noise in the original data, which reduces the erroneous effect of noise on the trending expression of the original data, and a hybrid dilated convolution network extracts the features of the different sensory fields of the noise‐reduced data, which increases the richness of the extracted features and thus improves the accuracy of the modeling. Next, the extracted features are fed into the RUL prediction module to predict RUL; into the classification model in the domain adaptation module to divide the source and target domains; and into the distribution difference measurement model in the domain adaptation module to identify the feature distribution differences between the source and target domains, and inversely adjust the model parameters by reducing the distribution differences. Furthermore, domain invariant characteristics of the features in different receptive fields under multiple operating conditions are obtained to enhance the model's generalization ability and achieve RUL prediction across various operating conditions. Monte Carlo (MC) dropout simulation technology is used to quantify the uncertainty of prediction results. Finally, the effectiveness and superiority of the proposed method are verified using the prognostics and health management (PHM) 2012 bearing dataset. [ABSTRACT FROM AUTHOR]

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing energy management and extending the lifespan of batteries across various industries. However, the raw capacity data of these batteries is often noisy and exhibits complex nonlinear degradation patterns, especially due to capacity regeneration phenomena during operation, making precise RUL prediction a significant challenge. Although various deep learning-based methods have been proposed, their performance relies heavily on the availability of large datasets, and satisfactory prediction accuracy is often achievable only with extensive training samples. To overcome this limitation, we propose a novel method that integrates sequence decomposition algorithms with an optimized neural network. Specifically, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw capacity data, effectively mitigating the noise from capacity regeneration. Subsequently, Particle Swarm Optimization (PSO) is used to fine-tune the hyperparameters of the Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based prediction model was extensively tested on eight lithium-ion battery datasets from NASA and CALCE, demonstrating robust generalization capability, even with limited data. The experimental results indicate that the CEEMDAN-PSO-BiGRU model can reliably and accurately predict the RUL and capacity of lithium-ion batteries, providing a promising and reliable method for RUL prediction in practical applications. [ABSTRACT FROM AUTHOR]

It is difficult to obtain the damage information on large slewing bearings only from vibration signals. In addition, deep learning models trained on old samples do not achieve high accuracy in new tasks. Therefore, this paper uses vibration, temperature, and torque signals of slewing bearings to build a model. Meanwhile, we add attention mechanism to capture internal correlation of them to consider the related factors of remaining useful life (RUL) from multiple angles. The multivariable gated recurrent unit (GRU) based on attention mechanism gated recurrent unit (attention-MGRU) model is adopted to improve the prediction performance. On this foundation, a fine-tuning strategy is introduced to improve the generalization ability of the model. A full-life accelerated test is carried out on the slewing bearing test bench. The model proposed in this paper is compared with GRU prediction model, which utilizes vibration signals and multivariable GRU prediction model. Mean absolute error (MAE) and root-mean-square error (RMSE) are used as measurement indicators. Among different methods, three indicators generated by attention-MGRU show significant superiority. Moreover, the fine-tuned model performs better in new tasks compared with the original model. [ABSTRACT FROM AUTHOR]

Aiming at the problem of electrical life prediction of AC contactor individual, considering that characteristic information and degradation trend of different life stages are different, a comprehensive electrical life analysis method is proposed. First, based on uncertainty reasoning, initial electrical life is qualitatively evaluated to realize an early warning for contactor with poor performance. Secondly, real‐time monitoring of performance degradation inflection point is used to realize the determination of degradation stage. With degradation stage matching mechanism, the quantitative prediction of remaining useful electrical life is realized based on performance inflection point and Elman neural network, thus forming an effective monitoring of the operation state in full life cycle. It is verified by experiment that the proposed method can accurately evaluate initial life grade and predict remaining useful life after performance inflection point. Compared with other methods, the information of electrical life for contactor individual can be accurately and comprehensively obtained. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction of aeroengines not only enhances aviation safety and operational efficiency but also significantly lowers operational costs, offering substantial economic and social benefits to the aviation industry. Aiming at RUL prediction, this paper proposes a novel dual-dimension convolutional-attention (DDCA) mechanism. DDCA consists of two branches: one includes channel attention and spatial attention mechanisms, while the other applies these mechanisms to the inverted dimensions. Pooling and feature-wise pooling operations are employed to extract features from different dimensions of the input data. These branches operate in parallel to capture more complex temporal and spatial feature correlations in multivariate time series data. Subsequently, an end-to-end DDCA-TCN network is constructed by integrating DDCA with a temporal convolutional network (TCN) for RUL prediction. The proposed prediction model is evaluated using the C-MAPSS dataset and compared to several state-of-the-art RUL prediction models. The results show that the RMSE and SCORE metrics of DDCA-TCN decreased by at least 12.8% and 4.6%, respectively, compared to other models on the FD002 subset, and by at least 10.6% and 18.4%, respectively, on the FD004 subset, which demonstrates that the DDCA-TCN model exhibits excellent performance in RUL prediction, particularly under multiple operating conditions. [ABSTRACT FROM AUTHOR]

The development of modern reusable launchers, such as the Themis project with its LOX/LCH4 Prometheus engine, CALLISTO—a reusable VTVL-launcher first-stage demonstrator with a LOX/LH2 RSR2 engine, and SpaceX's Falcon 9 with its Merlin 1D engine, underscores the need for advanced control algorithms to ensure reliable engine operation. The multi-restart capability of these engines imposes additional requirements for throttling, necessitating an extended controller-validity domain to safely achieve low thrust levels across various operating regimes. This capability also increases the risk of component failure, especially as engine parameters evolve with mission profiles. To address this, our study evaluates the dynamic reliability of reusable rocket engines (RREs) and their subcomponents under different failure modes using multi-physics system-level modelling and simulation, with a particular focus on turbopump components. Transient condition modelling and performance analysis, conducted using EcosimPro-ESPSS software (version 6.4.34), revealed that turbopump components maintain high reliability under nominal conditions, with turbine blades demonstrating significant fatigue life even under varying thermal and mechanical loads. Additionally, the proposed predictive model estimates the remaining useful life of critical components, offering valuable insights for improving the longevity and reliability of turbopumps in reusable rocket engines. This study employs deterministic, thermally dependent structural simulations, with key control objectives including end-state tracking of combustion chamber pressure and mixture ratios and the verification of operational constraints, exemplified by the LUMEN demonstrator engine and the LE-5B-2 engine class. [ABSTRACT FROM AUTHOR]

In fields such as manufacturing and aerospace, remaining useful life (RUL) prediction estimates the failure time of high-value assets like industrial equipment and aircraft engines by analyzing time series data collected from various sensors, enabling more effective predictive maintenance. However, significant temporal diversity and operational complexity during equipment operation make it difficult for traditional single-scale, single-dimensional feature extraction methods to effectively capture complex temporal dependencies and multi-dimensional feature interactions. To address this issue, we propose a Dual-Path Interaction Network, integrating the Multiscale Temporal-Feature Convolution Fusion Module (MTF-CFM) and the Dynamic Weight Adaptation Module (DWAM). This approach adaptively extracts information across different temporal and feature scales, enabling effective interaction of multi-dimensional information. Using the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset for comprehensive performance evaluation, our method achieved RMSE values of 0.0969, 0.1316, 0.086, and 0.1148; MAPE values of 9.72%, 14.51%, 8.04%, and 11.27%; and Score results of 59.93, 209.39, 67.56, and 215.35 across four different data categories. Furthermore, the MTF-CFM module demonstrated an average improvement of 7.12%, 10.62%, and 7.21% in RMSE, MAPE, and Score across multiple baseline models. These results validate the effectiveness and potential of the proposed model in improving the accuracy and robustness of RUL prediction. [ABSTRACT FROM AUTHOR]

The increasing complexity of modern mechanical systems, especially rotating machinery, demands effective condition monitoring techniques, particularly deep learning, to predict potential failures in a timely manner and enable preventative maintenance strategies. Health monitoring data analysis, a widely used approach, faces challenges due to data randomness and interpretation difficulties, highlighting the importance of robust data quality analysis for reliable monitoring. This paper presents a two-part approach to address these challenges. The first part focuses on comprehensive data preprocessing using only feature scaling and selection via random forest (RF) algorithm, streamlining the process by minimizing human intervention while managing data complexity. The second part introduces a Recurrent Expansion Network (RexNet) composed of multiple layers built on recursive expansion theories from multi-model deep learning. Unlike traditional Rex architectures, this unified framework allows fine tuning of RexNet hyperparameters, simplifying their application. By combining data quality analysis with RexNet, this methodology explores multi-model behaviors and deeper interactions between dependent (e.g., health and condition indicators) and independent variables (e.g., Remaining Useful Life (RUL)), offering richer insights than conventional methods. Both RF and RexNet undergo hyperparameter optimization using Bayesian methods under variability reduction (i.e., standard deviation) of residuals, allowing the algorithms to reach optimal solutions and enabling fair comparisons with state-of-the-art approaches. Applied to high-speed bearings using a large wind turbine dataset, this approach achieves a coefficient of determination of 0.9504, enhancing RUL prediction. This allows for more precise maintenance scheduling from imperfect predictions, reducing downtime and operational costs while improving system reliability under varying conditions. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average RMSE of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average RMSE of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability. [ABSTRACT FROM AUTHOR]

Accurately estimating the remaining useful life (RUL) of batteries is crucial for optimizing maintenance, preventing failures, and enhancing reliability, thereby saving costs and resources. This study introduces a hybrid approach for estimating the RUL of a battery based on the firefly algorithm–neural network (FA–NN) model, in which the FA is employed as an optimizer to fine-tune the network weights and hidden layer biases in the NN. The performance of the FA–NN is comprehensively compared against two hybrid models, namely the harmony search algorithm (HSA)–NN and cultural algorithm (CA)–NN, as well as a single model, namely the autoregressive integrated moving average (ARIMA). The comparative analysis is based mean absolute error (MAE) and root mean squared error (RMSE). Findings reveal that the FA–NN outperforms the HSA–NN, CA–NN, and ARIMA in both employed metrics, demonstrating superior predictive capabilities for estimating the RUL of a battery. Specifically, the FA–NN achieved a MAE of 2.5371 and a RMSE of 2.9488 compared with the HSA–NN with a MAE of 22.0583 and RMSE of 34.5154, the CA–NN with a MAE of 9.1189 and RMSE of 22.4646, and the ARIMA with a MAE of 494.6275 and RMSE of 584.3098. Additionally, the FA–NN exhibits significantly smaller maximum errors at 34.3737 compared with the HSA–NN at 490.3125, the CA–NN at 827.0163, and the ARIMA at 1.16e + 03, further emphasizing its robust performance in minimizing prediction inaccuracies. This study offers important insights into battery health management, showing that the proposed method is a promising solution for precise RUL predictions. [ABSTRACT FROM AUTHOR]

In response to the current issue of low accuracy and robustness in the remaining useful life (RUL) model of lithium-ion batteries. In the framework of AdaBoost, a lithium-ion battery life prediction model based on an improved whale optimization algorithm to optimize the Kernel Extreme Learning Machine (IWOA-KELM) is proposed. The IWOA-KELM model is used as a weak predictor. A weighted voting mechanism is used to set a weight coefficient for each weak predictor and then combine the strong predictor of battery RUL. Constant current charge time, constant voltage charge time, internal resistance, and incremental capacity curves peak were extracted from the Cycle data set as health features to accurately describe battery degradation. Pearson correlation coefficient and Savitzky-Golay filter preprocessed health features. Tent chaotic mapping is used to initialize whale populations and maintain their diversity. The iterative updating strategy of the hunting speed control factor is introduced to reduce the probability of the local optimal case of the whale optimization algorithm. The kernel function parameters and regularization parameters of KELM are optimized by IWOA to improve the model prediction ability. After verification, the RUL error of the method proposed in this article can be as accurate as 4 cycles. [ABSTRACT FROM AUTHOR]

Accurate prediction of the state of health (SOH) and remaining useful life (RUL) of batteries is essential for ensuring their safe and stable operation. Given the strong correlation between SOH and RUL, it is imperative to employ a joint prediction approach for a comprehensive evaluation of the actual battery aging condition. Therefore, this paper proposes a novel approach for jointly predicting SOH and RUL of lithium-ion batteries, which is based on the optimization of health features and fusion of multiple models. Multiple health features (HFs) are extracted from the charging voltage curve, and the fusion HF is optimized using kernel principal component analysis (KPCA) to leverage the complementary advantages of multiple features. Deep Gaussian process regression (DGPR) is employed to address the issue of lacking confidence interval expression in the prediction process. Combined with particle swarm optimization (PSO) and bidirectional long short-term memory (Bi-LSTM) network, fusion models of PSO-DGPR and Bi-LSTM-PSO-DGPR are developed for the prediction of SOH and RUL, respectively. In order to achieve the joint prediction of SOH and RUL, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is utilized to decompose the SOH prediction results, obtaining reliable capacity data for RUL prediction and eliminating local regeneration fluctuations caused by capacity regeneration effects. Furthermore, CALCE and NASA data sets are employed for validation. The experimental results demonstrate a significant reduction in the prediction error, compared to other prediction methods, thereby enhancing the accuracy and reliability of SOH and RUL prediction. [ABSTRACT FROM AUTHOR]

Accurately predicting the Remaining Useful Life (RUL) of lithium-ion batteries is crucial for battery management systems. Deep learning-based methods have been shown to be effective in predicting RUL by leveraging battery capacity time series data. However, the representation learning of features such as long-distance sequence dependencies and mutations in capacity time series still needs to be improved. To address this challenge, this paper proposes a novel deep learning model, the MLP-Mixer and Mixture of Expert (MMMe) model, for RUL prediction. The MMMe model leverages the Gated Recurrent Unit and Multi-Head Attention mechanism to encode the sequential data of battery capacity to capture the temporal features and a re-zero MLP-Mixer model to capture the high-level features. Additionally, we devise an ensemble predictor based on a Mixture-of-Experts (MoE) architecture to generate reliable RUL predictions. The experimental results on public datasets demonstrate that our proposed model significantly outperforms other existing methods, providing more reliable and precise RUL predictions while also accurately tracking the capacity degradation process. Our code and dataset are available at the website of github. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction is an essential task in ensuring reliability in intelligent manufacturing. Recent advances in deep learning-based data-driven methods have shown promising results. However, one non-ignorable challenge is that distribution shift across various machine individuals often results in a performance decline. Domain adaptation approaches appear to be effective in tackling this issue, whereas they often require sufficient unlabeled target data, which is causally infeasible in prognostic tasks in practical scenarios. In this paper, we discuss the significance of prognostic generalization for RUL prediction, and a domain generalization-based scheme is proposed. A domain conditional invariance and specificity disentanglement network (DCISD) is proposed to learn domain conditional-invariant and domain-specific information simultaneously in a unified network. Domain conditional-invariant features are extracted through conditional domain adversarial learning and samples are conditioned by multiple RUL fuzzy sets. Domain-specific features correlated to individual degradation patterns are disentangled to promote sufficiency of degradation information. Moreover, a degradation dynamics-based augmentation method is proposed to mitigate domain imbalance following the degradation dynamics in the latent space. Two bearing run-to-failure datasets are utilized to evaluate the proposed method. Comparative and ablation studies validate the method effectiveness and superiority. [ABSTRACT FROM AUTHOR]

More and more organisations are trying to install tool health analytics dashboards for CNC machines to avoid unexpected failures, maintain machining accuracy, and optimise tool change. This paper aims at developing a cyber physical production system framework for a smart tool health management system to prescribe the optimum cutting parameters to managers/operators for optimising the remaining useful life and/or material removal rate at a predefined surface finish (individually or simultaneously). This is achieved by developing (i) a machine learning algorithm to predict the remaining useful life of a cutting tool, (ii) regression models to prescribe optimum cutting parameters (iii) a machine learning algorithm for anomaly detection, and (iv) a knowledge-based system for chip conditions and tool life curves. Experiments are designed and conducted based on Taguchi L-27 orthogonal array with varying combinations of cutting parameters during the milling of a difficult to machine material (AISI H13 tool steel). The effect of cutting parameters is analysed statistically; using analysis of variance (ANOVA), response tables, and main effect plots; to prescribe optimum cutting parameters based on managerial requirements. A novel knowledge-based system is also presented that updates knowledge and information about the chip colour at different health conditions of a tool. The present work will be a significant step towards improving productivity, product quality, and reducing maintenance costs by providing practitioners with an active decision support tool that will assist them to confidently adopt optimum management and control strategies within an Industry 4.0 environment. [ABSTRACT FROM AUTHOR]

The availability of condition-monitoring data has increased due to internet of things (IoT) technologies, providing information on various parameters like vibration, temperature, current, and voltage. Cloud computing and big data facilitate the prevention of failures and estimation of remaining useful life through advanced mathematical models and artificial intelligence (AI) techniques. These enable prompt and suitable maintenance actions. This article conducts a systematic review of digital transformation technologies, including cloud computing, edge computing, AI, machine learning (ML), and TinyML, in predictive maintenance for conveyor belt systems. This article reviews how these digital transformation technologies improve predictive maintenance strategies for conveyor belts. The systematic review summarizes the results and challenges of various methodologies used in conveyor belt systems and suggests areas for further research. This paper aimed to serve as a useful resource for researchers, practitioners, and industry professionals seeking insights into current predictive maintenance technologies for conveyor belt systems. The takeaways of the review are expected to ignite discussion on efficient and proactive maintenance strategies and promote the development of innovative solutions for ensuring the reliability and longevity of conveyor belt systems in the digital era. [ABSTRACT FROM AUTHOR]

Due to the complex and diverse operating conditions of rolling bearings, it is difficult to accurately predict remaining useful life (RUL) of rolling bearings via traditional prediction models. Besides, when a rolling bearing malfunctions, only single channel or single-domain degradation information is considered to bear RUL prediction, and the prediction effect of existing RUL prediction methods is greatly limited. Therefore, in order to address the above issue, a novel method abbreviated as CNN-GRU-MSA with multi-channel feature fusion is proposed for RUL prediction of rolling bearings. Firstly, the statistical features related to the time series are calculated, and the similarity features are constructed based on the obtained statistical features. Then, the sensitive features are selected and fused through specific indicators. Finally, the dual-channel feature fusion is performed to construct a health indicator (HI), which is input into the proposed CNN-GRU-MSA model for training and achieving RUL prediction of rolling bearings. The effectiveness of the proposed method is validated using two benchmark datasets (i.e. IEEE PHM 2012 challenge datasets and XJTU-SY datasets). Experimental results demonstrate the superiority and effectiveness of the proposed method over other similar prediction methods in bearing RUL prediction. [ABSTRACT FROM AUTHOR]

For the safety and reliability of the system, Remaining Useful Life (RUL) prediction is considered in many industries. The traditional machine learning techniques must provide more feature representation and adaptive feature extraction. Deep learning techniques like Long Short-Term Memory (LSTM) achieved an excellent performance for RUL prediction. However, the LSTM network mainly relies on the past few data, which may only capture some contextual information. This paper proposes a hybrid combination of Convolution Neural Network (CNN) and LSTM (CNN+LSTM) to solve this problem. The proposed hybrid model predicts how long a machine can operate without breaking down. In the proposed work, 1D horizontal and vertical signals of the mechanical bearing are first converted to 2D images using Continuous Wavelet Transform (CWT). These 2D images are applied to CNN for key feature extraction. Ultimately, these key features are applied to the LSTM deep neural network for predicting the RUL of a mechanical bearing. A PRONOSTIA data is utilized to demonstrate the performance of the proposed model and compare the proposed model with other state-of-the-art methods. Experimental results show that our proposed CNN+LSTM-based hybrid model achieved higher accuracy (98%) with better robustness than existing methods. [ABSTRACT FROM AUTHOR]

ABSTRACT Rolling bearings are crucial components in a wide variety of machinery. Monitoring their conditions and predicting their remaining useful life (RUL) is vital to prevent unexpected breakdowns, optimize maintenance schedules, and reduce operational costs. This article proposes an approach based on adaptive continuous deep belief networks (ACDBN) and improved kernel extreme learning machine (KELM) to predict the RUL of rolling bearings. In the proposed approach, the ACDBN model is used for extracting hidden fault features and the distance between the initial health state and the real‐time degradation state is used to construct a health indicator (HI). Then, a hybrid kernel extreme learning machine prediction model optimized by the sparrow search algorithm (SSA‐KELM) is proposed to estimate the RUL using the extracted HIs. The SSA is used to find the optimal parameters of the KELM model. The proposed method has been assessed using existing bearing datasets. The obtained results indicate that the proposed method successfully improves RUL prediction accuracy compared to existing approaches in the literature. [ABSTRACT FROM AUTHOR]

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]

Different methods have been developed to predict power devices' remaining useful life (RUL). The existing methods need to specify the failure thresholds corresponding to failure precursors of power devices based on historical data. However, there might be heterogeneity in failure threshold between different devices despite being from the same batch, which can severely affect the RUL prediction performance. Aiming at this problem, this article proposes an RUL prediction method based on the non‐linear Wiener process considering the failure threshold uncertainty. To incorporate the failure threshold uncertainty into the RUL prediction, the truncated normal distribution is employed to characterize this uncertainty. The maximum‐likelihood estimation method is used to estimate the model parameters based on the historical degradation data. Then, the parameters can be dynamically updated by the Bayesian paradigm each time a new piece of condition monitoring (CM) data of the interested device in service is observed. This makes the predicted RUL dependent on the real‐time health conditions of the interested device in service. The effectiveness of the proposed method is validated with the power cycling testing results of SiC MOSFETs. Experimental results reveal that the proposed method can reduce the prediction error by 10.273%. [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]

In recent years, there has been growing interest in employing predictive methods to forecast the remaining useful life of industrial equipment. However, the challenge lies in how to take advantage of the dynamic predictive information to facilitate the maintenance of decision‐making. This problem becomes particularly challenging for complex industrial systems consisting of multiple components with economic dependencies. This paper aims at providing an effective maintenance strategy for multi‐component systems based on predictive information, while considering economic dependencies among different system components. To this end, a dynamic predictive maintenance (PdM) strategy that minimizes the mean maintenance cost over a decision period is proposed, where both long‐term and short‐term policies are integrated into the decision‐making framework. Specifically, the long‐term policy is formulated using predictions derived from historical degradation data through a Long Short‐Term Memory (LSTM) model. Concurrently, real‐time monitoring data is employed to forecast imminent degradation in components, serving as a basis for determining the necessity of short‐term adjustments. This paper embeds the consideration of economic dependencies among components within the maintenance strategy design and employs hierarchical clustering to establish an effective and efficient maintenance grouping policy. The experimental results demonstrate that our proposed strategy significantly outperforms conventional approaches, including block‐based and age‐based maintenance, resulting in substantial cost savings. The proposed strategy is also compared with a similar version without grouping, and the results verify the added value of the optimal maintenance grouping policy in cost reduction. Moreover, a comprehensive analysis of the proposed method is provided, including the impact of different inspection costs and inspection intervals on maintenance decision‐making, which can provide insightful guidance to various PdM scenarios in practice. [ABSTRACT FROM AUTHOR]

Introduction: Accurately predicting the remaining mechanical equipment is of great significance for ensuring the safe operation of the equipment and improving economic efficiency. Methods: To accurately assess the mechanical equipment degradation, predict its remaining useful life, and ensure efficient, stable, and safe operation, a degradation and life prediction model for mechanical equipment based on multivariate stochastic processes is studied. The study innovatively predicts the remaining life of mechanical equipment using multivariate stochastic processes, and facilitates the correlation analysis between performance indicators based on the characteristics of Copula functions. Results and discussion: The results showed that the Root Mean Squared Error value of the prediction results based on the trivariate Wiener process was 2.58, which decreased by 46.91% and 35.82% compared with the univariate and bivariate Wiener processes, respectively. The prediction value based on the trivariate gamma process was 3.49, which decreased by 44.95% and 40.54% compared with the univariate and bivariate gamma processes, respectively. In conclusion, the degradation and life prediction model with multivariate stochastic processes can effectively assess the mechanical equipment degradation and predict its remaining useful life. This provides an important reference for the maintenance and management of mechanical equipment, improving equipment efficiency and extend its service life. [ABSTRACT FROM AUTHOR]

In contemporary industrial systems, the prediction of remaining useful life (RUL) is recognized as a valuable maintenance strategy for health management due to its ability to monitor equipment operational status in real time and ensure the safety of industrial production. Current studies have largely concentrated on deep learning (DL) techniques, leading to a shortage of RUL prediction methods that utilize deep reinforcement learning (DRL). To further enhance application and research, this paper introduces a novel approach to RUL prediction based on DRL, specifically using a combination of Convolutional Neural Network-Bidirectional Long Short-Term Memory Network (CNN-BiLSTM) and the Deep Deterministic Policy Gradient (DDPG) algorithm. The proposed method reframes the conventional task of estimating RUL as a Markov decision process (MDP), effectively integrating the feature extraction capabilities of DL with the decision-making abilities of DRL. Initially, a hybrid CNN-BiLSTM is employed to establish an agent that can extract degradation features from raw signals. Subsequently, the DDPG algorithm within DRL is leveraged to develop the RUL prediction mechanism, completing the MDP by defining appropriate action spaces and reward functions. The agent, through repeated trials and optimization, learns to map the current operational state of the rolling bearing to its remaining service life. Validation analysis was performed on the intelligent maintenance systems (IMS) bearing dataset. The findings suggest that the DRL-based approach outperforms the current methodologies, demonstrating a superior performance in root mean square error (MSE) and MSE metrics. The predicted outcomes align more closely with the actual lifespan values. [ABSTRACT FROM AUTHOR]

Gears and bearings play vital roles as essential transmission components in mechanical drivetrains. Accurately predicting the remaining useful life (RUL) of these components is paramount to ensure optimal performance and prevent unexpected failures. To enhance the precision of RUL prediction, a novel method has been developed which involves constructing health indicators (HI) and implementing an adaptive dynamic weighting (ADW) on a gated dual attention unit (GDAU). The process commences by extracting multi-dimensional time-frequency domain features from vibration signals, which are then refined using an improved kernel principal component analysis (Adaptive Kernel Principal Component Analysis - AKPCA) to extract key components. Subsequently, the constructed HI is fine-tuned through an optimization process utilizing the exponentially weighted moving average method. Finally, the ADW strategy dynamically adjusts the input weights of the HI, and the GDAU model is employed to predict the RUL of gears and bearings. Experiment and comparison results have validated the effectiveness and advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The inter-shaft bearing is the core component of a high-pressure rotor support system of a high-thrust aero engine. One of the most challenging tasks for a PHM is monitoring its working condition. However, considering that in the bearing rotor system of a high-thrust aero engine bearings are prone to wear failure due to unbalanced or misaligned faults of the rotor system, especially in harsh environments, such as those at high operating loads and high rotation speeds, bearing wear can easily evolve into serious faults. Compared with aero engine fault diagnosis and RUL prediction, relatively little research has been conducted on bearing condition monitoring. In addition, considering how to evaluate future performance states with limited time series data is a key problem. At the same time, the current deep neural network model has the technical challenge of poor interpretability. In order to fill the above gaps, we developed a new framework of a residual space–time feature fusion focusing module named RTCA-Net, which focuses on solving the key problem. It is difficult to accurately monitor the wear state of aero engine inter-shaft bearings under special working conditions in practical engineering. Specifically, firstly, a residual space–time structure module was innovatively designed to capture the characteristic information of the metal dust signal effectively. Secondly, a feature-focusing module was designed. By adjusting the change in the weight coefficient during training, the RTCA-Net framework can select the more useful information for monitoring the wear condition of inter-shaft bearings. Finally, the experimental dataset of metal debris was verified and compared with seven other methods, such as the RTC-Net. The results showed that the proposed RTCA-Net framework has good generalization, superiority, and credibility. [ABSTRACT FROM AUTHOR]

Wind turbine blades bear the maximum cyclic load and varying self-weights in turbulent wind environments, which accelerate the propagation of cracks that ultimately progress from minor faults, resulting in blade failure and significant maintenance and shutdown costs. To address this issue, this paper proposes an adaptive control strategy for the blade's useful life. The control system is divided into the inner control loop and the outer control loop. The outer loop is based on the Paris crack propagation model combined with a particle filtering algorithm and calculates the degradation of the blade life under the crack threshold conditions provided by the operation and maintenance strategy to determine the parameter settings of the inner-loop load-shedding controller. The control strategy we propose can balance the load-shedding capability of the controller with the fatigue load of the pitch actuator while considering the predefined remaining useful blade life in the operation and maintenance strategy, avoiding unplanned downtime and reducing maintenance costs. [ABSTRACT FROM AUTHOR]

Machine failures during the manufacturing process can have severe consequences, causing extensive downtime and financial losses. Hence, predictive maintenance (PdM) plays a crucial role within the Industrial Internet of Things (IIoT) by estimating the remaining useful life (RUL) of machines so that proactive maintenance measures can be taken to mitigate potential failures and minimize disruptions. RUL estimation is enabled by gathering and processing the data sensed by sensors mounted on and around the machines at the central server (base station or cloud) after analyzing the failure patterns. However, this approach imposes a significant load on network bandwidth and leads to poor response time for the monitoring system because a large volume of sensed data has to be transmitted to the central server for processing. Moreover, due to the singularity of the computing resource, many problems, such as inefficient resource utilization, frequent offloading, single-point failure, etc., have become major challenges. To address these issues, this article proposes an edge computing-enabled predictive maintenance framework called "Collaborative Predictive Maintenance Framework" (CollabRULe), which first identifies the optimal locations for edge server placement in the deployment region by considering the QoS parameters, such as energy, delay and connectivity. Then, it uses federated learning for predictive maintenance by estimating the RUL of the machines. Simulation results show that the proposed mechanism effectively minimizes the overall network energy consumption and end-to-end delay by ≈ 60 % and ≈ 35 % , respectively, as compared to state-of-the-art approaches. Furthermore, it shows significant improvement in the accuracy of RUL prediction as compared to its counterparts. [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]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Standard ML relies on ample data, but limited availability poses challenges. Transfer learning offers a solution by leveraging pre-existing knowledge. Yet many methods require access to the model's internal aspects, limiting applicability to white box models. To address this, Tsai, Chen and Ho introduced Black Box Adversarial Reprogramming for transfer learning with black box models. While tested primarily in image classification, this paper explores its potential in time series classification, particularly predictive maintenance. We develop an adversarial reprogramming concept tailored to black box time series classifiers. Our study focuses on predicting the Remaining Useful Life of rolling bearings. We construct a comprehensive ML pipeline, encompassing feature engineering and model fine-tuning, and compare results with traditional transfer learning. We investigate the impact of hyperparameters and training parameters on model performance, demonstrating the successful application of Black Box Adversarial Reprogramming to time series data. The method achieved a weighted F1-score of 0.77, although it exhibited significant stochastic fluctuations, with scores ranging from 0.3 to 0.77 due to randomness in gradient estimation. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are cornerstones of renewable technologies, which is why they are used in many applications, specifically in electric vehicles and portable electronics. The accurate estimation of the remaining useful life (RUL) of a battery is pertinent for durability, efficient operation, and stability. In this study, we have proposed an approach to predict the RUL of a battery using partial discharge data from the battery cycles. Unlike other studies that use complete cycle data and face reproducibility issues, our research utilizes only partial data, making it both practical and reproducible. To analyze this partial data, we applied various deep learning methods and compared multiple models, among which ConvLSTM showed the best performance, with an RMSE of 0.0824. By comparing the performance of ConvLSTM at various ratios and ranges, we have confirmed that using partial data can achieve a performance equal to or better than that obtained when using complete cycle data. [ABSTRACT FROM AUTHOR]