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This systematic review presents a critical analysis of advanced machine learning (ML) and deep learning (DL) approaches for predicting the remaining useful life (RUL) of electric vehicle (EV) batteries. Conducted in accordance with PRISMA guidelines and using a novel adaptation of the Downs and Black (D&B) scale, this study evaluates 89 research papers and provides insights into the evolving landscape of RUL estimation. Our analysis reveals an evolving landscape of methodological approaches, with different techniques showing distinct capabilities in capturing complex degradation patterns in EV batteries. While recent years have seen increased adoption of DL methods, the effectiveness of different approaches varies significantly based on application context and data characteristics. However, we also uncover critical challenges, including a lack of standardized evaluation metrics, prevalent overfitting problems, and limited dataset sizes, that hinder the field's progress. To address these, we propose a comprehensive set of evaluation metrics and emphasize the need for larger and more diverse datasets. The review introduces an innovative clustering approach that provides a nuanced understanding of research trends and methodological gaps. In addition, we discuss the ethical implications of DL in RUL estimation, addressing concerns about privacy and algorithmic bias. By synthesizing current knowledge, identifying key research directions, and suggesting methodological improvements, this review serves as a central guide for researchers and practitioners in the rapidly evolving field of EV battery management. It not only contributes to the advancement of RUL estimation techniques but also sets a new standard for conducting systematic reviews in technology-driven fields, paving the way for more sustainable and efficient EV technologies. [ABSTRACT FROM AUTHOR]

The aero engine is crucial for the safe flight of aircraft. Predicting its remaining useful life allows for timely maintenance, thereby preventing potential flight accidents. Deep learning’s powerful data mining capabilities provide a novel approach to predicting the remaining useful life of aero engines using vast amounts of historical data. However, traditional deep learning methods often only analyze temporal data correlations, overlooking the complex non-Euclidian spatial relationship between multiple sensor data. In addition, they seldom address the uncertainties in data or the prediction process, which impacts the reliability of their results. To solve these problems, we propose a probabilistic prediction method for aero engines that leverages Bayesian networks and the graph attention transformer. First, sensor data undergo preprocessing using a convolutional denoising autoencoder. Then, to extract the complex non-Euclidian spatial relationships between sensors, we construct a graph convolution network. In this network, sensor signal features serve as node characteristics, and the relationships between sensors are measured by cosine similarity. An attention mechanism assigns different weights to sensor nodes to improve the expressive ability of the graph convolution network. In this paper, the multi-head attention is integrated into the graph convolution network. Following the integration, node characteristics are aggregated with the assigned weights in the proposed graph attention network. Furthermore, to achieve joint extraction of the spatiotemporal relationships of sensor data, the graph attention mechanism is integrated into the transformer’s temporal-multi-attention module. This integration combines the spatial feature extraction strengths of the graph attention transformer with the temporal feature extraction abilities of the transformer model. The extracted spatiotemporal joint features are often utilized to predict the remaining useful life of the aero engine. At the same time, a Bayesian network quantifies the prediction uncertainty of the model using Gaussian mixture distribution and variational inference. Optimal approximate distribution parameters are obtained by minimizing the Kullback–Leibler divergence between the real posterior and approximate distributions. Consequently, the loss function of the proposed model network consists of two parts: minimum mean square error, which reflects the distance between the predicted remaining useful life and the actual remaining useful life, and distribution approximation error measured by Kullback–Leibler divergence. Unlike traditional point value predictions, our probabilistic prediction method yields not only an estimated remaining useful life but also a corresponding confidence interval, which provides a more reliable foundation for subsequent maintenance and decision-making. In addition, we validated the proposed method using an aero engine experimental dataset, comparing its performance against other methods through both comparative and ablation experiments. The results demonstrate the effectiveness of the proposed method and its superiority over existing methods.

In the ever-evolving landscape of industrial connectivity, significant strides have been made in the integration of 5th generation (5G) cellular technology with Industrial Internet of Things (IIoT) systems. At the same time, data-driven analytics has become an effective tool for leveraging information from interconnected industrial devices, enabling organizations to gain valuable insights and make informed decisions. However, among these advancements, the holistic perspective of end-to-end analysis related to their integration remains a critical aspect that has yet to be comprehensively addressed. To this end, we investigate 5G IIoT network architectures that support automated guided vehicles (AGVs) on a factory floor as an illustrative example. In particular, we leverage real sensor data collected by AGVs to estimate their remaining useful life (RUL) using a deep learning (DL)-based pipeline. We conduct an in-depth analysis to assess the compatibility of 5G New Radio infrastructures with the aforementioned case study, focusing on round trip time (RTT) requirements and emphasizing the inter-dependencies between communication network and data-driven application.

Abstract Forecasting the remaining useful life (RUL) is a crucial aspect of prognostics and health management (PHM), which has garnered significant attention in academic and industrial domains in recent decades. The accurate prediction of RUL relies on the creation of an appropriate degradation model for the system. In this paper, a general representation of diffusion process models with three sources of uncertainty for RUL estimation is constructed. According to time-space transformation, the analytic equations that approximate the RUL probability distribution function (PDF) are inferred. The results demonstrate that the proposed model is more general, covering several existing simplified cases. The parameters of the model are then calculated utilizing an adaptive technique based on the Kalman filter and expectation maximization with Rauch-Tung-Striebel (KF-EM-RTS). KF-EM-RTS can adaptively estimate and update unknown parameters, overcoming the limits of strong Markovian nature of diffusion model. Linear and nonlinear degradation datasets from real working environments are used to validate the proposed model. The experiments indicate that the proposed model can achieve accurate RUL estimation results.

Abstract Industry 4.0 represents the fourth industrial revolution, which is characterized by the incorporation of digital technologies, the Internet of Things (IoT), artificial intelligence, big data, and other advanced technologies into industrial processes. Industrial Machinery Health Management (IMHM) is a crucial element, based on the Industrial Internet of Things (IIoT), which focuses on monitoring the health and condition of industrial machinery. The academic community has focused on various aspects of IMHM, such as prognostic maintenance, condition monitoring, estimation of remaining useful life (RUL), intelligent fault diagnosis (IFD), and architectures based on edge computing. Each of these categories holds its own significance in the context of industrial processes. In this survey, we specifically examine the research on RUL prediction, edge-based architectures, and intelligent fault diagnosis, with a primary focus on the domain of intelligent fault diagnosis. The importance of IFD methods in ensuring the smooth execution of industrial processes has become increasingly evident. However, most methods are formulated under the assumption of complete, balanced, and abundant data, which often does not align with real-world engineering scenarios. The difficulties linked to these classifications of IMHM have received noteworthy attention from the research community, leading to a substantial number of published papers on the topic. While there are existing comprehensive reviews that address major challenges and limitations in this field, there is still a gap in thoroughly investigating research perspectives across RUL prediction, edge-based architectures, and complete intelligent fault diagnosis processes. To fill this gap, we undertake a comprehensive survey that reviews and discusses research achievements in this domain, specifically focusing on IFD. Initially, we classify the existing IFD methods into three distinct perspectives: the method of processing data, which aims to optimize inputs for the intelligent fault diagnosis model and mitigate limitations in the training sample set; the method of constructing the model, which involves designing the structure and features of the model to enhance its resilience to challenges; and the method of optimizing training, which focuses on refining the training process for intelligent fault diagnosis models and emphasizes the importance of ideal data in the training process. Subsequently, the survey covers techniques related to RUL prediction and edge-cloud architectures for resource-constrained environments. Finally, this survey consolidates the outlook on relevant issues in IMHM, explores potential solutions, and offers practical recommendations for further consideration.

Abstract Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.

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]

Recurrent neural networks (RNNs), as a specialized neural network architecture for processing time series data, are increasingly vital in predicting the remaining useful life (RUL) and tool wear. However, RNNs have inherent sequence dependency, which makes it difficult to effectively parallelize when processing input data, significantly reducing training efficiency. To address these limitations, this paper proposes a temporal-spatial encoder convolutional network (TSECN) for RUL and tool wear prediction. This model uses the temporal feature extraction (TFE) module is adopted to excavate temporal features parallelly and dynamically weigh the features of different timesteps to improve its feature representation capability. Meanwhile, the spatial feature extraction (SFE) module is employed to excavate both local and global spatial features, which are then fused by a new feature fusion layer to enhance its prediction accuracy. The feature compression module is utilized to reduce the computational complexity and mitigate over-fitting. Finally, the regression prediction module is used to realize an accurate prediction of the target variable. Based on the C-MAPSS and PHM2010 datasets, experiments were conducted to assess the performance of the TSECN model, which shows that the TSECN model surpasses the state-of-the-arts in both the RUL and wear prediction tasks in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

The rapid growth of electrical energy demands raises the need for the modernization of distribution grids. Medium-voltage (MV) aged cables are infrastructures facing significant challenges that can compromise the security of supply and reduce the reliability of power grids. To address the challenges, there is a growing interest in optimizing cable replacement and management strategies. This comprehensive review focuses on the technical challenges and innovations associated with MV cable replacement, highlighting defect detection, lifetime estimation, reliability assessment, and management strategies. Various methods for detecting and monitoring cable defects and discussing their advantages and limitations are surveyed. Moreover, different models and techniques for estimating the remaining useful life of MV cables are explored, emphasizing the importance of accurate predictions for assessing cable reliability and optimizing replacement schedules. Furthermore, emerging technologies that enhance cable management strategies are also highlighted. This review provides insights and recommendations for future research and development, paving the way for the sustainable evolution of power grids.

To improve the accuracy of predicting the remaining useful life (RUL) of computer numerical control (CNC) machine tool components, this study proposes a novel method. In the method, a condition monitoring platform for components is built to obtain component operation information. The collected information is processed to acquire signal features with better trend. The Weibull model is modified via the fusion of internal signal features and external operating information. Accordingly, a Weibull regression model that fully considers the operating condition information of components is established. The fminsearch function is applied to solve the WRM with complete parameterization, and the optimal parameter estimates of the model are obtained. The RUL prediction method is demonstrated using a specific example. Multiple indexes are used to evaluate the model performance. Furthermore, the validity of the model is verified by comparison. The proposed method can obtain more accurate RUL prediction results of the components. It is of great significance to the health operation of CNC machine tools.

Accurate prediction of Remaining Useful Life (RUL) is crucial for Prognostics and Health Management (PHM), particularly in predictive maintenance strategies aimed at ensuring the reliability of industrial systems. This study compares two approaches for RUL prediction of aircraft engines: a deep learning-based one-dimensional Convolutional Neural Network (CNN-1D) and a traditional Decision Tree (DT) algorithm, using data from the C-MAPSS dataset. The results show that the CNN-1D model significantly outperforms the DT model, achieving a Root Mean Square Error (RMSE) of 21.44 on the training set and 27.12 on the test set, compared to the DT model’s RMSE of 23.83 and 28.93, respectively. These findings highlight the superior capability of deep learning techniques in RUL estimation, underscoring their importance in PHM and predictive maintenance applications.

Abstract 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 Accurately predicting the remaining useful life (RUL) of lithium-ion (Li-ion) batteries is vital for improving battery performance and safety in applications such as consumer electronics and electric vehicles. While the prediction of RUL for these batteries is a well-established field, the current research refines RUL prediction methodologies by leveraging deep learning techniques, advancing prediction accuracy. This study proposes AccuCell Prodigy, a deep learning model that integrates auto-encoders and long short-term memory (LSTM) layers to enhance RUL prediction accuracy and efficiency. The model’s name reflects its precision (“AccuCell”) and predictive strength (“Prodigy”). The proposed methodology involves preparing a dataset of battery operational features, split using an 80–20 ratio for training and testing. Leveraging 22 variations of current (critical parameter) across three Li-ion cells, AccuCell Prodigy significantly reduces prediction errors, achieving a mean square error of 0.1305%, mean absolute error of 2.484%, and root mean square error of 3.613%, with a high R-squared value of 0.9849. These results highlight its robustness and potential for advancing battery health management.

The growing popularity of battery-powered products, such as electric vehicles and wearable devices, has increasingly motivated the need to predict the remaining life of lithium-based batteries. This study proposes a method for predicting the remaining life of lithium-based batteries based on a hybrid neural network. First, variational modal decomposition (VMD) was used for noise reduction to maximize retention of the original information and prevent capacity degradation. Second, the trend of capacity decline after noise reduction was modeled and predicted using the combination of bidirectional long short-term memory (BiLSTM) and Monte Carlo (MC) dropout. Finally, experiments were conducted to show that the new method based on the VMD-MC-BiLSTM network achieves good performance for predicting the remaining life of a lithium battery with sufficient confidence level, thereby providing a new approach for optimizing the management of lithium batteries. [ABSTRACT FROM AUTHOR]

In the realm of lithium-ion batteries (LIBs), issues like material aging and capacity decline contribute to performance degradation or potential safety hazards. Predicting remaining useful life (RUL) serves as a crucial method of assessing the health of batteries, thereby enhancing reliability and safety. To reduce the complexity and improve the accuracy and applicability of early RUL predictions for LIBs, we proposed a Mamba-based state space model for early RUL prediction. Due to the impacts of abnormal data, we first use the interquartile range (IQR) method with a sliding window for data cleansing. Subsequently, the top three highest correlated features are selected, and only the first 300 cycling data are used for training. The model has the ability to make forecasts using these few historical data. Extensive experiments are conducted using CALCE CS2 datasets. The MAE, RMSE, and RE are less than 0.015, 0.019, and 0.0261; meanwhile, R 2 is higher than 0.99. Compared to the baseline approaches (CNN, BiLSTM, and CNN-BiLSTM), the average MAE, RMSE, and RE of the proposed approach are reduced by at least 29%, 21%, and 36%, respectively. According to the experimental results, the proposed approach performs better in terms of accuracy, robustness, and efficiency. [ABSTRACT FROM AUTHOR]

Aero-engines, particularly turbofan engines, are highly complex systems that play a critical role in the aviation industry. As core components of modern aircraft, they provide the thrust necessary for flight and are essential for safe and efficient operations. However, the complexity and interconnected nature of these engines also make them vulnerable to failures and, in the context of intelligent systems, potential cyber-attacks. Ensuring the secure and reliable operation of these engines is crucial as disruptions can have significant consequences, ranging from costly maintenance issues to catastrophic accidents. The innovation of this article lies in a proposed method for obtaining key points. The research method is based on convolution and the basic shape of convolution. Through feature fusion, a self-convolution operation, a half operation, and derivative operation on the original feature data of the engine, two key points of the engine in the entire lifecycle are obtained, and these key points are analyzed in detail. Finally, the key point-based acquisition method and statistical data analysis were applied to the engine's health planning and lifespan prediction, and the results were validated on the test set. The results indicate that the key point-based method proposed in this paper has promising prospects. [ABSTRACT FROM AUTHOR]

Ceramic Matrix Composites (CMCs), specifically SiC/SiC composites, represent a significant innovation in aerospace material technology, offering superior performance over traditional nickel-based superalloys in high-temperature turbine blade applications. This study presents a novel techno-economic assessment, filling a critical gap in the literature by directly comparing the economic and technical viability of CMCs versus superalloys. Unlike previous studies, which primarily focus on technical performance or cost analysis independently, this work integrates both aspects, providing a holistic comparison across key economic metrics, including acquisition, machining, maintenance, and recycling costs. The results demonstrate that SiC/SiC blades offer a 15–20% higher Net Present Value (NPV) and a 17% greater Internal Rate of Return (IRR) over a 20-year lifecycle than superalloys. Despite higher initial costs, CMCs achieve an estimated 2 to 3 years reduction in payback period, mainly due to their superior thermal and creep resistance, leading to fewer maintenance interventions and longer operational lifetimes. Although machining costs for CMCs are higher, these are more than offset by the long-term savings achieved through improved fuel efficiency and lower maintenance costs. A comprehensive sensitivity analysis, incorporating fluctuations in discount rates and material costs, further validates the economic robustness of CMCs in various operational scenarios. This study is the first to compare CMCs and superalloys, offering new insights into the financial implications of material selection in aerospace manufacturing. The findings present critical engineering recommendations that empower aerospace manufacturers and decision-makers to optimise material selection for improved efficiency and cost-effectiveness in high-performance turbine applications. [ABSTRACT FROM AUTHOR]

ABSTRACT Batteries are prevalent energy storage devices, and their failures can cause huge losses such as the shutdown of entire systems. Therefore, the prognostic health management of batteries to increase their availability is highly desirable. This work focuses on improving the serviceability of batteries for wireless sensor networks (WSNs) deployed in remote and hard‐to‐reach places. We propose an active management strategy such that the batteries in a network will attain similar end‐of‐life times, in addition to lifetime extension. The fundamental idea is to adaptively adjust the node quality‐of‐service (QoS) to actively manage their degradation processes, while ensuring a minimum level of network QoS. The framework first executes a prognostic algorithm that can predict the remaining useful life (RUL) of a battery, given its assigned node‐level QoS. A Bayesian optimization framework with an augmented Lagrangian method has been adopted to efficiently solve the developed black‐box constrained optimization problem. A Matlab Simulink model based on a truss bridge structure health monitoring network is built considering the battery aging and temperature effects. Compared with the benchmark models, the proposed strategy demonstrates a more extended network lifespan and uniform working time ratio. [ABSTRACT FROM AUTHOR]

This study highlights the increasing demand for battery-operated applications, particularly electric vehicles (EVs), necessitating the development of more efficient Battery Management Systems (BMS), particularly lithium-ion (Li-ion) batteries used in energy storage systems (ESS). This research addresses some of the key limitations of current BMS technologies, with a focus on accurately predicting the remaining useful life (RUL) of batteries, which is a critical factor for ensuring operational efficiency and sustainability. Real-time data are collected from sensors via an Internet of Things (IoT) device and processed using Arduino Nano, which extracts values for input into a Long Short-Term Memory (LSTM) model. This model employs the National Aeronautics and Space Administration (NASA) Li-battery dataset and current, voltage temperature, and cycle values to predict the battery RUL. The proposed model demonstrates significant forecasting precision, attaining a root mean square error (RMSE) of 0.01173, outperforming all comparative models. This improvement facilitates more effective decision-making in BMS, particularly in resource allocation and adaptability to transient conditions. However, the practical implementation of real-time data acquisition systems at a scale and across diverse environments remains challenging. Future research will focus on enhancing the generalizability of the model, expanding its applicability to broader datasets, and automating data ingestion to minimize integration challenges. These advancements are aimed at improving energy efficiency in both industrial and residential applications in accordance with the Sustainable Development Goals (SDGs) of the UN. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life (RUL) of high-speed railroad contact networks can guide the scientific development of maintenance plans, thus reducing economic waste. Based on this, this paper proposes an Attention-LSTM-CNN-based Stacking deep integration method for RUL prediction of contact networks in high-speed railroads. The method first cleans the collected contact network data and analyzes the feature contribution, then selects the stacked base model based on Spearman correlation. Finally, the long short-term convolutional memory neural network (Attention-LSTM-CNN), random forest (RF), and K-nearest neighbor (KNN) are used as the base learners in the Stacking integrated model to predict the RUL of the contact network with the improved attention mechanism; the hyperparameters of each base learner are optimized using genetic algorithm. Using detection data from a line in southeast China as an arithmetic example for comparison experiments, the results show that the RMSE of the prediction performance evaluation index of Attention-LSTM-CNN based on Stacking is 0.068, and the RMSE of the proposed model decreases by 26.9% and 32% compared with the individual models RF and KNN, respectively. Compared with the hybrid model consisting of RF, KNN, the RMSE of the proposed model decreased by 31.3%: the prediction after genetic optimization has an RMSE of 0.045 and MAPE of 4.32%, which is 33.8% reduction in RMSE and 13.8% reduction in MAPE compared to those before optimization. [ABSTRACT FROM AUTHOR]

To function effectively, nuclear power plants rely on the effective filtration of air, water, and process fluids, examples of which include inlet sea water, reactor coolant, plant drinking water, and moderator purification. Filtration assets degrade over time, which impairs their filtering performance and reduces the flow rate. Being able to determine the remaining useful life (RUL) of a filter could result in benefits, particularly when moving from a time-based to a condition-based maintenance strategy that would optimize the filter replacement procedure and reduce early replacement of filters that are still fit for purpose. For many filter applications, a time-based strategy is sufficient. For strategically important assets, such as fueling machines, there are benefits to be gained from the development of predictive maintenance strategies. In this paper, we propose a predictive condition-based strategy using differential pressure data as a proxy for filter health. The key objective in this work was the creation of a model that could predict a filter asset RUL. The differential pressure for 7 to 14 days is predicted by a heuristic-based regression model of the history of each filter. This approach has been demonstrated using a civil nuclear generation application but could be applied to wider applications. While this model is still undergoing on-site evaluation, it has been estimated that there will be an operationally significant lifetime cost reduction. [ABSTRACT FROM AUTHOR]

Lithium‐ion batteries (LIBs) are extensively utilized in electric vehicles due to their high energy density and cost‐effectiveness. LIBs exhibit dynamic and nonlinear characteristics, which raise significant safety concerns for electric vehicles. Accurate and real‐time battery state estimation can enhance safety performance and prolong battery lifespan. With the rapid advancement of big data, machine learning (ML) holds substantial promise for state estimation. This paper systematically reviews several common ML algorithms, detailing the basic principles of each and illustrating their structures with flowcharts. We compare the advantages and disadvantages of various methods. Subsequently, we discuss feature extraction techniques employed in recent studies for estimating state of charge (SOC), state of health (SOH), state of power (SOP), and remaining useful life (RUL), as well as the application of these ML methods in state estimation. Finally, we discuss the challenges associated with using ML methods for state estimation and outline future development trends. [ABSTRACT FROM AUTHOR]

The document "NDT Info" is a comprehensive resource on non-destructive testing and condition monitoring, covering techniques like ultrasonic, radiography, eddy current, magnetic, and acoustic emission. It provides access to technical reports, standards, and databases in the field and welcomes contributions for further insights. Topics discussed include mechanical fault diagnosis, artificial intelligence in wind turbines, defect detection in aeronautic materials, and X-ray computed tomography for inspection of heat exchangers and salt hydrate beds. [Extracted from the article]

This paper proposes a fault precognition system designed for predictive maintenance in bearing systems aimed at improving Remaining Useful Life (RUL) estimation accuracy. This study makes use of the Pronostia-bearing dataset, a recognized standard for RUL prediction and predictive maintenance. It includes vibration data captured by accelerometer sensors along two axes (X and Y), which shows how bearings deteriorate under different operation circumstances. The extensive size of the dataset, which includes several bearings experiencing progressive deterioration, guarantees strong validation of the suggested fault precognition and RUL prediction system in actual maintenance situations. The system utilizes the Pronostia bearing dataset, employing time-domain feature extraction, automated feature ranking, and fault pattern classification through K-means clustering with Silhouette Coefficients. A core component of the system is an Autoencoder-LSTM model, which identifies early fault occurrences by analyzing reconstruction loss thresholds—quantitative measures of deviation between observed and reconstructed data. These thresholds serve as indicators of anomalous behaviour, distinguishing normal operations from fault-prone data clusters. The system then estimates RUL using various LSTM variants, including Vanilla LSTM, BiLSTM, CNN-LSTM, StackLSTM, ConvLSTM and Encoder-Decoder LSTM, with performance evaluated using Mean Squared Error (MSE) and R² scores. The results demonstrate that incorporating fault precognition into the system significantly enhances prediction accuracy, facilitating proactive maintenance and improving operational reliability. [ABSTRACT FROM AUTHOR]

The deep learning model has attracted widespread attention in the field of rolling bearing remaining useful life (RUL) prediction due to its advantages of less reliance on prior knowledge, high accuracy, and strong generalization. However, a large number of prediction models use very complicated artificial feature extraction and selection methods to build the original input features of the deep learning model and health indicator. These approaches do not fully exploit the capabilities of deep learning models as they continue to heavily rely on prior knowledge, The accuracy of their predictions largely hinges on the quality of the input features, and the generalization of manually crafted features remains uncertain. To address these challenges, in this paper, an end-to-end prediction model for the remaining useful life of rolling bearings is proposed, which is divided into three modules. First, a short-term Fourier transform module is incorporated into the model to automatically obtain the time–frequency information of the signal. Then, the convolutional next (ConvNext) module, which is a simple and efficient pure convolutional neural network, is utilized to extract features from the spectrogram. Finally, we capture the short-term dependence and long-term dependence by two parallel channels Transformer and self-attention convolutional long short-term memory (SA-ConvLSTM), and the self-attention mechanism is employed for the adaptive prediction of the bearing's remaining useful life. Through integration with artificial intelligence, this method proposes a high-performance solution for predicting the remaining useful life of bearings. It has minimal reliance on manual labor, stronger fitting capabilities, and can be widely used for predicting the remaining useful life of bearings. [ABSTRACT FROM AUTHOR]

In recent years, preventive maintenance has emerged as a focal point of research in the aerospace field. The concept of equipment maintenance, exemplified by prognosis and health management (PHM), has permeated every aspect of development and design. Extracting degradation features presents a fundamental and challenging task for health assessment and remaining useful life prediction. To facilitate the efficient operation of the incipient fault diagnosis model, this paper proposes a data-driven feature extraction process for converters, which consists of two main stages. First, feature extraction and comparison are conducted in the time domain, frequency domain, and time–frequency domain. By employing wavelet decomposition and the Hilbert transform method, a highly correlated time–frequency domain feature is obtained. Second, an improved feature selection approach that combines the ReliefF algorithm with the correlation coefficient is proposed to effectively minimize redundancy within the feature subset. Furthermore, an incipient fault diagnosis model is established using neural networks, which verifies the effectiveness of the data-driven feature extraction process presented herein. Experimental results indicate that this method not only maintains fault diagnosis accuracy but also significantly reduces training time. [ABSTRACT FROM AUTHOR]

In this study, we address the critical gap in predictive maintenance systems regarding the absence of a robust provenance system and specification. To tackle this issue, we propose a provenance system based on the PROV-O schema, designed to enhance explainability, accountability, and transparency in predictive maintenance processes. Our framework facilitates the collection, processing, recording, and visualization of provenance data, integrating them seamlessly into these systems. We developed a prototype to evaluate the effectiveness of our approach and conducted comprehensive user studies to assess the system's usability. Participants found the extended PROV-O structure valuable, with improved task completion times. Furthermore, performance tests demonstrated that our system manages high workloads efficiently, with minimal overhead. The contributions of this study include the design of a provenance system tailored for predictive maintenance and a specification that ensures scalability and efficiency. [ABSTRACT FROM AUTHOR]

In recent years, research on the state of health (SOH) and remaining useful life (RUL) estimation methods for lithium-ion batteries has garnered significant attention in the new energy sector. Despite the substantial volume of annual publications, a systematic approach to quantifying and analyzing these contributions is lacking. This study focuses on selecting pertinent literature related to lithium-ion battery SOH and RUL estimation from CNKI and WOS databases, spanning January 2010 to December 2023. Employing bibliometric tools such as VOSviewer and CiteSpace, we conduct visual analyses to elucidate the current state, development trends, and research frontiers in this domain. Our examination encompasses scholarly activity, year-wise literature distribution, international collaboration networks, structural dissemination, and journal contributions. The findings indicate an upward trend in annual publication output, with China, the United States, the United Kingdom, and Canada at the forefront of collaborative research efforts. China is increasingly recognized as a pivotal hub for global scholarly partnerships. Notably, Harbin Institute of Technology, Beijing Institute of Technology, Chongqing University, Chinese Academy of Sciences, and Beijing Jiaotong University are the top institutions in China and the world in terms of publications. The Journal of Energy Storage emerges as a prominent periodical, acclaimed both domestically and internationally for its rigorous standards and high-quality articles. Based on the research content from CNKI and WOS, VOSviewer clusters the main research directions into three themes: aging mechanisms, SOH estimation methods, and RUL prediction methods. Keywords such as 'online estimation ', 'hybrid models', and 'artificial neural networks' feature prominently, signaling a strong emphasis on artificial intelligence strategies. The study concludes with a prospective outlook on imminent research trajectories regarding the health and longevity estimations of lithium-ion batteries, highlighting the critical need for ongoing innovation and collaboration in this essential field. [ABSTRACT FROM AUTHOR]

Remaining useful life (RUL) prediction plays a fundamental role in the prognostics and health management of mechanical equipment. Consequently, extensive research has been devoted to estimating the RUL of mechanical equipment. Owing to the development of modern advanced sensor technologies, a significant amount of monitoring data is recorded. Traditional methods, such as machine-learning-based methods and statistical-data-driven methods, are ineffective in matching when faced with big data thus leading to poor predictions. As a result, deep-learning-based methods are extensively utilized due to their efficient capability to excavate deep features and realize accurate predictions. However, most deep-learning-based methods only provide point estimations and ignore the prediction uncertainty. To address this limitation, this paper proposes a parallel prognostic network to sufficiently excavate the degradation features from multiple dimensions for more accurate RUL prediction. In addition, accurate calculation of model evidence is extremely difficult when dealing with big data so the Monte Carlo dropout is employed to infer the model weights under low computational cost and high scalability to obtain a probabilistic RUL prediction. Finally, the C-MAPSS aero-engine dataset is employed to validate the proposed dual-channel framework. The experimental results illustrate its superior prediction performance compared to other deep learning methods and the ability to quantify prediction uncertainty. [ABSTRACT FROM AUTHOR]

The remaining useful life (RUL) prediction of rolling bearings is crucial for optimizing maintenance schedules, reducing downtime, and extending machinery lifespan. However, existing multi-channel feature fusion methods do not fully capture the correlations between channels and time points in multi-dimensional sensor data. To address the above problems, this paper proposes a multi-channel feature fusion algorithm based on a hybrid attention mechanism and temporal convolutional networks (TCNs), called MCHA-TFCN. The model employs a dual-channel hybrid attention mechanism, integrating self-attention and channel attention to extract spatiotemporal features from multi-channel inputs. It uses causal dilated convolutions in TCNs to capture long-term dependencies and incorporates enhanced residual structures for global feature fusion, effectively extracting high-level spatiotemporal degradation information. The experimental results on the PHM2012 dataset show that MCHA-TFCN achieves excellent performance, with an average Root-Mean-Square Error (RMSE) of 0.091, significantly outperforming existing methods like the DANN and CNN-LSTM. [ABSTRACT FROM AUTHOR]

This research paper introduces a comprehensive proactive maintenance architecture designed for large-scale industrial machinery systems. The proposed architectural framework integrates supervised and unsupervised machine learning business processes in order to enhance maintenance capabilities. The primary objective of this architecture is to enhance operational efficiency and reduce the occurrence of problems in industrial equipment. The collection of data on the state of industrial machinery is conducted through the utilization of sensors that are attached to it. The recommended framework offers modules that might potentially implement capabilities such as immediate anomaly detection, pre-failure status prediction, and assessment of remaining usable life. We offer a prototype implementation to verify the appropriateness of the proposed framework for testing purposes. The prototype utilizes a simulation framework, Cooja, to model a sensor network. The concept entails the collection of status data from industrial machinery by each sensor. The prototype utilizes a machine learning library for data streams, the MOA framework, to design and implement a business process for anomaly detection using unsupervised machine learning, as well as a business process for early machine fault prediction using supervised machine learning. In addition, deep learning libraries are employed to construct a business process that predicts the remaining operational lifespan of industrial machinery that is anticipated to experience failure. Furthermore, we examined the efficacy of the prototype's integrated business protocols in this investigation. The proposed framework aligns effectively with software architectures designed to offer maintenance functionalities for industrial machinery, as indicated by our research findings. [ABSTRACT FROM AUTHOR]

This comprehensive review explores data-driven methodologies that facilitate the prognostics and health management (PHM) of centrifugal pumps (CPs) while utilizing both vibration and non-vibration sensor data. This review investigates common fault types in CPs, while placing a specific emphasis on artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) techniques, for fault diagnosis and prognosis. A key innovation of this review is its in-depth analysis of cutting-edge methods, such as adaptive thresholding, hybrid models, and advanced neural network architectures, aimed at accurately predicting the remaining useful life (RUL) of CPs under varying operational conditions. This review also addresses the limitations and challenges of the current AI-driven methodologies, offering insights into potential solutions. By synthesizing these methodologies and presenting practical applications through case studies, this review provides a forward-looking perspective to empower industry professionals and researchers with effective strategies to ensure the reliability and efficiency of centrifugal pumps. These findings could contribute to optimizing industrial processes and advancing health management strategies for critical components. [ABSTRACT FROM AUTHOR]

In various industry sectors, predicting the real-life availability of milling applications poses a significant challenge. This challenge arises from the need to prevent inefficient blade resource utilization and the risk of machine breakdowns due to natural wear. To ensure timely and accurate adjustments to milling processes based on the machine's cutting blade condition without disrupting ongoing production, we introduce the Fused Data Prediction Model (FDPM), a novel temporal hybrid prediction model. The FDPM combines the static and dynamic features of the machines to generate simulated outputs, including average cutting force, material removal rate, and peripheral milling machine torque. These outputs are correlated with real blade wear measurements, creating a simulation model that provides insights into predicting the wear progression in the machine when associated with real machine operational parameters. The FDPM also considers data preprocessing, reducing the dimensional space to an advanced recurrent neural network prediction algorithm for forecasting blade wear levels in milling. The validation of the physics-based simulation model indicates the highest fidelity in replicating wear progression with the average cutting force variable, demonstrating an average relative error of 2.38% when compared to the measured mean of rake wear during the milling cycle. These findings illustrate the effectiveness of the FDPM approach, showcasing an impressive prediction accuracy exceeding 93% when the model is trained with only 50% of the available data. These results highlight the potential of the FDPM model as a robust and versatile method for assessing wear levels in milling operations precisely, without disrupting ongoing production. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is transforming the future of industries by introducing new paradigms. To address data privacy and other challenges of decentralization, research has focused on Federated Learning (FL), which combines distributed Machine Learning (ML) models from multiple parties without exchanging confidential information. However, conventional FL methods struggle to handle situations where data samples have diverse features and sizes. We propose a Hybrid Federated Learning solution with label synchronization to overcome this challenge. Our FedLabSync algorithm trains a feed-forward Artificial Neural Network while alerts that it can aggregate knowledge of other ML architectures compatible with the Stochastic Gradient Descent algorithm by conducting a penalized collaborative optimization. We conducted two industrial case studies: product inspection in Bosch factories and aircraft component Remaining Useful Life predictions. Our experiments on decentralized data scenarios demonstrate that FedLabSync can produce a global AI model that achieves results on par with those of centralized learning methods. [ABSTRACT FROM AUTHOR]

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While lithium-ion batteries (LIBs) have pushed the progression of electric vehicles (EVs) as a viable commercial option, they introduce their own set of issues regarding sustainable development. This paper investigates how using end-of-life LIBs in stationary applications can bring us closer to meeting the sustainable development goals (SDGs) highlighted by the United Nations. We focus on how this practice can support three of these goals, namely Goal 7: Affordable and Clean Energy, Goal 12: Responsible Consumption and Production, and Goal 13: Climate Action. We present a literature review that details the aging mechanisms of LIBs, namely battery degradation, state of charge, state of health, depth of discharge, remaining useful life, and battery management systems. Then, we thoroughly examine the environmental and economic benefits of using second-life EV batteries in stationary applications and how they align with the SDGs. Our review of the literature summarizes the most relevant research in battery aging, giving a foundation for further research and allowing effective legislation to be written around EVs. Additionally, our examination of the benefits of using second-life batteries motivates initiatives for sustainable practices, helping both corporations and legislators orient their ideals towards the SDGs. [ABSTRACT FROM AUTHOR]

The increasing demand for reliable and safe Lithium-ion (Li-ion) batteries requires more accurate estimation of state of health (SOH) and remaining useful life (RUL) prediction. However, the inherent complexity and non-linear dynamics of Li-ion batteries present specific challenges to traditional methods of SOH modeling. Although particle filter (PF) techniques can handle nonlinear dynamics, they still face challenges, including particle degeneracy and loss of diversity, that reduce their ability to effectively model the nonlinear degradation mechanisms of batteries. To tackle these limitations, this paper presents a novel artificial intelligence-driven PF (AI-PF) technology for battery health modeling and prognosis. The main contributions of the AI-PF technique are as follows: (1) A novel dynamic sample degeneracy detection method is proposed to provide real-time assessment of particle weights so as to promptly identify degeneracy and improve computational efficiency. (2) An adaptive crossover and mutation strategy is proposed to reallocate low-weight particles and maintain particle diversity to improve modeling and RUL forecasting accuracy. The effectiveness of the AI-PF framework is validated through systematic evaluations carried out using benchmark models and well-recognized battery datasets. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are critical components of various advanced devices, including electric vehicles, drones, and medical equipment. However, their performance degrades over time, and unexpected failures or discharges can lead to abrupt operational interruptions. Therefore, accurate prediction of the remaining useful life is essential to ensure device safety and reliability. Conventional RUL prediction methods typically rely on regression analysis, signal processing, and machine learning techniques to assess battery conditions such as charge/discharge cycles, voltage, temperature, and durability. Although effective, these approaches are constrained by their dependence on large amounts of labeled data and the necessity for complex feature engineering to capture battery physical characteristics. In this study, we propose an approach that employs deep transfer learning to address these limitations. By leveraging pretrained model weights, the proposed method significantly improves the efficiency and accuracy of RUL prediction even under limited training data conditions. Furthermore, we investigate the impact of external environmental factors and physical battery characteristics on RUL prediction precision, thereby contributing to a more robust and reliable prediction framework. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University 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.)

ABSTRACT In an effort to explore methods to improve overall system reliability, this systematic literature review (SLR) explores the current state of dynamic fault trees (DFTs) for complex, redundant systems that exhibit performance degradation over time. DFTs were first developed to manage and optimize redundant resources of computer system components, but have also been successfully used to manage redundant resources in aircraft and naval fuel distribution systems, transportation systems, industrial plants, computer networks, cardiac assist systems, and spacecraft systems. More recent DFT gates were added to identify, filter, and manage time‐dependent failures to minimize their impacts on the overall system performance and reliability, and have been applied to aircraft fault management systems, nuclear power plants, and alarm management systems. This SLR also explored the current state of technology for dynamically calculating not only the current capacity of system components, but also predicting their remaining useful life (RUL), remaining useful performance (RUP), and capacity degradation profiles. This is essential in understanding capacity degradation in the broader context of optimization of resources in complex, redundant systems. This SLR analysis revealed two gaps in the current technology and application of DFTs to complex systems. First, the usage of DFTs for failure management requires that the DFT is developed with a priori knowledge of specific failure modes, meaning that DFTs are inherently not scalable for new failures that are discovered. Second, although DFTs do address failure management, there are inherent limitations in the detection and application of partial failures or capacity degradation in the DFT infrastructure. [ABSTRACT FROM AUTHOR]

In the context of Industry 5.0, manufacturing systems are driven by human-centered production processes, assigning high-level supervisory tasks to operators. This necessitates that machines can perform low-level decision-making actions. This paper presents a novel hybrid heterogeneous prognosis algorithm designed to autonomously inspect the cutting edges of drill-bits and to forecast their Remaining Useful Life along with the associated probability density function. The algorithm leverages specific force coefficients from spindle power and feed axis current measurements, as features correlated with tool wear, to detect tool brittle failures. Additionally, flank wear is automatically measured through a specifically conceived image processing algorithm, using thresholding, convolutional filters, and edge detection techniques. Direct tool wear measurements are analyzed by a hybrid prognosis algorithm, fusing particle filter and multi-layer perceptron, to predict drill-bits' remaining useful lives. The proposed solution offers several advantages. It reduces the need for extensive experimental run-to-failure tests typically required for training standard machine learning algorithms. Instead, it allows for real-time adaptation, even in scenarios involving untested and varying cutting process conditions. Furthermore, it utilizes both indirect wear observations during cutting operations and direct wear observations during setup times (e.g. tool changes, workpiece changes), without interrupting the ongoing process. Exponent of Kronenberg's models for specific force coefficients was found to be sensitive to tool wear. Prognosis could correctly predict the 67% of end-of-lives with an average prognosis horizon of 30%. [ABSTRACT FROM AUTHOR]

Ensuring the long-term safe usage of lithium-ion batteries hinges on accurately estimating the State of Health and predicting the Remaining Useful Life (RUL). This study proposes a novel prediction method based on a AT-CNN-BiLSTM architecture. Initially, key parameters such as voltage, current, temperature, and SOH are extracted and averaged for each cycle to ensure the uniformity and reliability of the input data. The CNN is utilized to extract deep features from the data, followed by BiLSTM to analyze the temporal dependencies in the data sequences. Since multidimensional parameter data are used to predict the SOH trend of lithium-ion batteries, an attention mechanism is employed to enhance the weight of highly relevant vectors, improving the model's analytical capabilities. Experimental results demonstrate that the CNN-BiLSTM-Attention model achieves an absolute error of 0 in RUL prediction, an value greater than 0.9910 , and a MAPE value less than 0.9003. Comparative analysis with hybrid neural network algorithms such as LSTM, BiLSTM, and CNN-LSTM confirms the proposed model's high accuracy and stability in SOH estimation and RUL prediction. [ABSTRACT FROM AUTHOR]

This review paper comprehensively analyzes the prognosis of rotating machines (RMs), focusing on mechanical-flaw and remaining-useful-life (RUL) estimation in industrial and renewable energy applications. It introduces common mechanical faults in rotating machinery, their causes, and their potential impacts on RM performance and longevity, particularly in wind, wave, and tidal energy systems, where reliability is crucial. The study outlines the primary procedures for RUL estimation, including data acquisition, health indicator (HI) construction, failure threshold (FT) determination, RUL estimation approaches, and evaluation metrics, through a detailed review of published work from the past six years. A detailed investigation of HI design using mechanical-signal-based, model-based, and artificial intelligence (AI)-based techniques is presented, emphasizing their relevance to condition monitoring and fault detection in offshore and hybrid renewable energy systems. The paper thoroughly explores the use of physics-based, data-driven, and hybrid models for prognosis. Additionally, the review delves into the application of advanced methods such as transfer learning and physics-informed neural networks for RUL estimation. The advantages and disadvantages of each method are discussed in detail, providing a foundation for optimizing condition-monitoring strategies. Finally, the paper identifies open challenges in prognostics of RMs and concludes with critical suggestions for future research to enhance the reliability of these technologies. [ABSTRACT FROM AUTHOR]

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

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]

Accurate prediction of the remaining useful life (RUL) of bearings is crucial for maintaining the reliability and efficiency of industrial systems. This study introduces a novel methodology integrating advanced machine learning and optimization techniques to address this challenge. (1) A transformer-attention model was developed to process segmented vibration signals, effectively capturing complex patterns. The model showed better performance than traditional approaches, with an RMSE of 0.989. (2) A Deep Neural Network (DNN) was designed to predict the extended RUL of bearings after laser shock peening (LSP) remanufacturing. The fruit fly optimization (FFO) algorithm was employed to optimize the remanufacturing parameters; a 29.33% improvement was achieved in fitness compared to the baseline. (3) The DNN model predictions were validated against Finite Element Analysis (FEA) simulations, with a low relative error of 2.5% to 5.8%; the model showed good accuracy in capturing the effects of optimized LSP parameters on bearing life extension. [ABSTRACT FROM AUTHOR]

This paper introduces the DeNet-Mamba-DC-SCSSA network, an advanced solution for predicting the Remaining Useful Life (RUL) of lithium-ion batteries, crucial for the safety and efficiency management of electric vehicles. Combining the robust Denoising Enhancement Network (DeNet), the Improved Sparrow Optimization Algorithm (SCSSA), the adept Mamba time-series model, and the proficient Dilated Convolution (DC), this model excels in precise noise handling and sophisticated feature extraction. DeNet diligently refines input data, mitigating noise interference, while Mamba skillfully captures sequential intricacies. DC, on the other hand, adeptly extracts features over varying time scales, ensuring meticulous RUL predictions.The model's efficacy was rigorously tested on NASA and CALCE datasets and was benchmarked against cutting-edge algorithms. Remarkably, it reduced average RE and RMSE by 48.59% and 21.45%, respectively, showcasing its superior performance and accuracy. Further evaluation on the CALCE dataset against the latest methods affirmed its leading predictive precision and stability.The model's robustness and practical applicability were further validated using real vehicle data from a new energy vehicle platform. In a challenging test, it accurately predicted the charging capacities corresponding to the mileage of four vehicles with minimal errors: 0.52 Ah, 1.03 Ah, 0.84 Ah, and 0.71 Ah. These results significantly surpassed those of other recent methods, highlighting the model's exceptional generalizability and potential for real-world applications in electric vehicle battery management. [ABSTRACT FROM AUTHOR]