Reliability Engineering & System Safety最新文献_第3页

Uncertainty evaluation of the debris flow impact considering spatially varying basal friction and solid concentration 考虑空间变化基础摩擦和固体浓度的泥石流影响的不确定性评价

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111283

Hongyu Luo , Limin Zhang , Jian He , Jiawen Zhou

{"title":"Uncertainty evaluation of the debris flow impact considering spatially varying basal friction and solid concentration","authors":"Hongyu Luo , Limin Zhang , Jian He , Jiawen Zhou","doi":"10.1016/j.ress.2025.111283","DOIUrl":"10.1016/j.ress.2025.111283","url":null,"abstract":"<div><div>The inherent spatial variability of soil is reported to significantly impact landslide debris behaviors. In this study, the effect of spatial variability on the inundation and impact processes of debris flow is investigated using a multi-phase depth-averaged model. The dynamic process of a debris flow, considering spatial variabilities of basal friction and initial solid concentration, is explored via Monte Carlo simulation. The results show that due to the flow channel constrain and spatial averaging, the influences of spatial variability on the global impact of debris flow are not significant. However, remarkable influences on the local impact are found. From the upstream of flow channel to the downstream of river, there is a decreasing trend in uncertainties regarding the material composition and flow dynamics at local spots. In the flow channel, the mean values of flow depths are smaller than those in the deterministic analysis, while those of flow velocities are larger. In the river, both the mean values of flow depths and velocities are close to those in the deterministic analysis while their variations remain significant even downstream of river. The findings provide insights into the spatial variability effects on debris flow impact and facilitate risk assessment.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111283"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of economic loss and recover process after earthquake base on nighttime light data and time series model 基于夜间灯光数据和时间序列模型的地震后经济损失及恢复过程估算

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111244

Jinpeng Zhao , Xiaojun Li , Su Chen

{"title":"Estimation of economic loss and recover process after earthquake base on nighttime light data and time series model","authors":"Jinpeng Zhao , Xiaojun Li , Su Chen","doi":"10.1016/j.ress.2025.111244","DOIUrl":"10.1016/j.ress.2025.111244","url":null,"abstract":"<div><div>This study develops a framework for predicting Nighttime Light (NTL) data trends without earthquakes and estimating economic losses and recovery processes after an earthquake. Utilizing Geographic Information System (GIS) technology, the framework unfolds through five stages: data acquisition and extraction, data transformation and calibration, model training and tuning for each 500 m x 500 m grid in the Jiuzhaigou earthquake area in Sichuan Province, China, and calculation of economic losses by comparing NTL predictions and actual observations. By integrating socio-economic characteristics and Damage Index data with NTL trends, and adjusting for model performance through hyperparameter tuning, this research quantifies economic impacts after an earthquake. Final estimates of economic losses and recovery are derived by allocating losses according to each grid’s calculated weight. This study not only predicts NTL data trends in scenarios without earthquake but also contributes novel methods for evaluating economic losses and recovery from earthquakes, presenting an effective framework for similar disaster impact studies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111244"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing power grid resilience during tropical cyclones: Deep learning-based real-time wind forecast corrections for dynamic risk prediction 增强热带气旋期间电网的恢复能力：基于深度学习的实时风预报校正动态风险预测

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111284

You Wu , Naiyu Wang , Xiubing Huang , Zhenguo Wang

{"title":"Enhancing power grid resilience during tropical cyclones: Deep learning-based real-time wind forecast corrections for dynamic risk prediction","authors":"You Wu , Naiyu Wang , Xiubing Huang , Zhenguo Wang","doi":"10.1016/j.ress.2025.111284","DOIUrl":"10.1016/j.ress.2025.111284","url":null,"abstract":"<div><div>Tropical cyclones (TCs) pose severe risks to power transmission systems, yet conventional Numerical Weather Prediction (NWP) models lack the resolution to resolve sub-kilometer wind dynamics critical for infrastructure risk assessment. This study introduces a Real-time Wind Forecast Correction (RWFC) model, a deep learning framework that dynamically refines mesoscale NWP forecasts during TCs by assimilating multi-source observational data. The RWFC integrates Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNN-BiLSTM) to capture spatiotemporal wind-terrain interactions, with a custom loss function balancing accuracy and conservative bias for proactive risk mitigation. Validated during Typhoon Hagupit (2020) in Zhejiang Province, China, the RWFC reduced wind speed and direction mean absolute errors (MAE) by 78 % (6.47 to 1.41 m/s) and 50 % (53.57° to 26.79°), respectively, compared to raw NWP forecasts. By interpolating corrections from sparse observational sites, it achieved province-scale MAE reductions of 56 %, demonstrating robust generalizability. When applied to Zhejiang’s transmission grid, RWFC lowered the number of projected high-risk towers by 98 %, enabling precise, terrain-sensitive risk predictions. The framework bridges NWP’s physical rigor with deep learning’s adaptive capacity, offering a scalable solution for enhancing grid resilience during evolving TCs. This work advances real-time disaster management by transforming coarse forecasts into actionable, high-resolution risk insights for critical infrastructure.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111284"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep auto-encoded Conditional Gaussian Mixture Model for warranty claims forecasting 用于保修索赔预测的深度自编码条件高斯混合模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-23 DOI: 10.1016/j.ress.2025.111230

Wael Hassanieh , Abdallah Chehade , Vasiliy Krivtsov

{"title":"Deep auto-encoded Conditional Gaussian Mixture Model for warranty claims forecasting","authors":"Wael Hassanieh , Abdallah Chehade , Vasiliy Krivtsov","doi":"10.1016/j.ress.2025.111230","DOIUrl":"10.1016/j.ress.2025.111230","url":null,"abstract":"<div><div>Forecasting warranty claims enables (i) identifying and rectifying quality and reliability problems at the early stages of production, (ii) improving future product designs, and (iii) efficiently allocating financial resources for warranty and maintenance. Nevertheless, precise forecasting of warranty claims is challenging due to the data maturation phenomenon, which is characterized by challenges that include market demand variability, reporting delays, heterogeneous production quality, and customer late claim rush. This paper proposes the Deep auto-encoded Conditional Gaussian Mixture Model (DCGMM) for warranty claims forecasting. DCGMM is a novel hybrid Bayesian and deep learning framework. The method learns a prior joint distribution between auto-encoded temporal latent embeddings extracted from early observed cumulative warranty claims and long-term mature warranty claims based on historical products. DCGMM then uses Bayesian inference to forecast the mature warranty claims of an in-service product of interest conditioned on its auto-encoded embeddings extracted from its limited early claims observations. DCGMM is robust to product variability and complex temporal trends because of its ability to identify different cluster or sub-population behaviors. Furthermore, learning a lower-dimensional space is essential to achieving tractable Bayesian inferences. We validate the performance of the DCGMM for warranty claims forecasting on a large-scale automotive case study and compare it to other state-of-the-art time-series forecasting algorithms.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111230"},"PeriodicalIF":9.4,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Quantitative analysis of risk propagation in urban rail transit: A novel ensemble learning method based on the structure of Bayesian Network 城市轨道交通风险传播定量分析：一种基于贝叶斯网络结构的集成学习方法

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-22 DOI: 10.1016/j.ress.2025.111280

Yuanxi Xu, Keping Li, Yanyan Liu

{"title":"Quantitative analysis of risk propagation in urban rail transit: A novel ensemble learning method based on the structure of Bayesian Network","authors":"Yuanxi Xu, Keping Li, Yanyan Liu","doi":"10.1016/j.ress.2025.111280","DOIUrl":"10.1016/j.ress.2025.111280","url":null,"abstract":"<div><div>The quantification of risk propagation in urban rail transit systems is a critical task to ensure the safe operations. In this study, a novel ensemble learning method based on Bayesian network structure learning is developed to describe the risk propagation mechanisms. The proposed model addresses the reliance problem on ordering and is capable to quantify more complex risk propagation paths. First, an information-based criterion and a score function are proposed to construct the initial propagation structure. Second, a structure constructing algorithm is introduced to generate multiple Bayesian networks, forming a Bayesian Forest. Finally, three applications of the Bayesian Forest are introduced: scenario inference, sensitivity analysis and risk propagation chain evaluation. Additionally, a case study is made on the application of the proposed model to the Shanghai metro system to verify its effectiveness. The results validate the rationality of the ensemble learning method by analyzing multiple risk propagation paths. The interaction characteristics are explicitly described by sensitivity of risk factors and the significance of the risk propagation chain is accurately evaluated.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111280"},"PeriodicalIF":9.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144147879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixed shock model for the multi-state system with a two-phase degradation process under Markov environment 马尔可夫环境下具有两阶段退化过程的多态系统混合激波模型

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-22 DOI: 10.1016/j.ress.2025.111254

Hao Lyu , Hengxin Wei , Hualong Xie , Yimin Zhang

{"title":"Mixed shock model for the multi-state system with a two-phase degradation process under Markov environment","authors":"Hao Lyu , Hengxin Wei , Hualong Xie , Yimin Zhang","doi":"10.1016/j.ress.2025.111254","DOIUrl":"10.1016/j.ress.2025.111254","url":null,"abstract":"<div><div>Reliability analysis of multi-state systems is crucial in engineering, particularly when dependent competing failure processes arise from both degradation and shocks. Many existing models do not fully capture the dependencies between shock processes and their influence on system deterioration. This study develops a mixed shock model under a Markov environment, integrating a two-phase degradation process. The shock processes are characterized using a Poisson phase-type process, which accounts for dependencies in damage increments. System states evolve through distinct functional levels: perfect function, degraded function, and severely degraded function. Soft failure occurs when cumulative degradation exceeds a predefined threshold, whereas hard failure results from extreme shocks or the accumulation of damage. By employing the finite Markov chain imbedding approach and phase-type distribution, explicit reliability functions are formulated. A case study on a spool valve validates the model, demonstrating its applicability in evaluating the reliability of multi-state systems. The proposed model provides an enhanced framework for assessing the reliability of complex engineering systems, addressing dependencies in degradation and shock processes.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111254"},"PeriodicalIF":9.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144190021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers 可解释的AI引导高压断路器无监督故障诊断

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-22 DOI: 10.1016/j.ress.2025.111199

Chi-Ching Hsu , Gaëtan Frusque , Florent Forest , Felipe Macedo , Christian M. Franck , Olga Fink

{"title":"Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers","authors":"Chi-Ching Hsu , Gaëtan Frusque , Florent Forest , Felipe Macedo , Christian M. Franck , Olga Fink","doi":"10.1016/j.ress.2025.111199","DOIUrl":"10.1016/j.ress.2025.111199","url":null,"abstract":"<div><div>Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfunctioning mechanisms and usually can be monitored only if the CB is disconnected from the grid. To facilitate online condition monitoring while CBs remain connected, non-intrusive measurement techniques such as vibration or acoustic signals are necessary. Currently, CB condition monitoring studies using these signals typically utilize supervised methods for fault diagnostics, where ground-truth fault types are known due to artificially introduced faults in laboratory settings. This supervised approach is however not feasible in real-world applications, where fault labels are unavailable. In this work, we propose a novel unsupervised fault detection and segmentation framework for CBs based on vibration and acoustic signals. This framework can detect deviations from the healthy state. The explainable artificial intelligence (XAI) approach is applied to the detected faults for fault diagnostics. The specific contributions are: (1) we propose an integrated unsupervised fault detection and segmentation framework that is capable of detecting faults and clustering different faults with only healthy data required during training (2) we provide an unsupervised explainability-guided fault diagnostics approach using XAI to offer domain experts potential indications of the aged or faulty components, achieving fault diagnostics without the prerequisite of ground-truth fault labels. These contributions are validated using an experimental dataset from a high-voltage CB under healthy and artificially introduced fault conditions, contributing to more reliable CB system operation.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111199"},"PeriodicalIF":9.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bi-objective redundancy allocation problem in systems with mixed strategy: NSGA-II with a novel initialization 混合策略系统的双目标冗余分配问题：具有新初始化的NSGA-II

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-22 DOI: 10.1016/j.ress.2025.111279

Mateusz Oszczypała

{"title":"Bi-objective redundancy allocation problem in systems with mixed strategy: NSGA-II with a novel initialization","authors":"Mateusz Oszczypała","doi":"10.1016/j.ress.2025.111279","DOIUrl":"10.1016/j.ress.2025.111279","url":null,"abstract":"<div><div>The redundancy allocation problem (RAP) aims to maximize system availability while minimizing costs, subject to weight constraints. The solution to the bi-objective RAP is represented by a Pareto front, comprising non-dominated system configurations. Previous studies have focuses on refining processes such as dominance relationship determination, selection, crossover, and mutation. This paper enhances the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) by introducing a novel approach for generating the initial population. While genetic algorithms traditionally rely on random population generation, this work proposes Scaled Binomial Initialization (SBI), which adjusts the probability of generating binary numbers for subsequent individuals in the initial population. SBI improves the diversity of chromosomes encoding component allocation priorities within subsystems, resulting in greater solution dispersion in the search space and enhanced exploration of regions with extreme objective function values. SBI is specifically designed for indirect chromosome encoding, ensuring feasible solutions across the population in all generations, thereby eliminating the need for a penalty function. A continuous-time Markov chain was developed to estimate the availability of k-out-of-n subsystems with a mixed redundancy strategy. The proposed method was evaluated on four benchmarks: a series system, a series-parallel system, a complex bridge system, and a large-scale system. For small-scale systems, NSGA-II with both random initialization and SBI achieved comparable levels of effectiveness and diversity in the Pareto front. However, for large-scale systems, NSGA-II with SBI demonstrated significant advantages, as reflected in the performance metrics of the approximated Pareto front.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111279"},"PeriodicalIF":9.4,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Time-to-failure based deterioration factors of water networks: Systematic review and prioritization 基于失效时间的水网退化因素：系统审查和优先排序

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-21 DOI: 10.1016/j.ress.2025.111246

Beenish Bakhtawar , Tarek Zayed , Nehal Elshaboury

{"title":"Time-to-failure based deterioration factors of water networks: Systematic review and prioritization","authors":"Beenish Bakhtawar , Tarek Zayed , Nehal Elshaboury","doi":"10.1016/j.ress.2025.111246","DOIUrl":"10.1016/j.ress.2025.111246","url":null,"abstract":"<div><div>Amidst global scarcity, preventing pipeline failures in water distribution systems is crucial for maintaining a clean supply while conserving water resources. Numerous studies have modelled water pipeline deterioration; however, existing literature does not correctly understand the failure time prediction for individual water pipelines. Existing time-to-failure prediction models rely on available data, failing to provide insight into factors affecting a pipeline's remaining age until a break or leak occurs. The study systematically reviews factors influencing time-to-failure, prioritizes them using a magnitude-based fuzzy analytical hierarchy process, and compares results with expert opinion using an in-person Delphi survey. The final pipe-related prioritized failure factors include <em>pipe geometry, material type, operating pressure, pipe age, failure history, pipeline installation, internal pressure, earth</em> and <em>traffic loads</em>. The prioritized environment-related factors include <em>soil properties, water quality, extreme weather events, temperature,</em> and <em>precipitation</em>. Overall, this prioritization can assist practitioners and researchers in selecting features for time-based deterioration modelling. Effective time-to-failure deterioration modelling of water pipelines can create a more sustainable water infrastructure management protocol, enhancing decision-making for repair and rehabilitation. Such a system can significantly reduce non-revenue water and mitigate the socio-environmental impacts of pipeline ageing and damage.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111246"},"PeriodicalIF":9.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-guided graph isomorphism learning: A multi-task framework for fault diagnosis and remaining useful life prediction 注意引导图同构学习：故障诊断和剩余使用寿命预测的多任务框架

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-05-21 DOI: 10.1016/j.ress.2025.111209

Junyu Qi , Zhuyun Chen , Yun Kong , Wu Qin , Yi Qin

{"title":"Attention-guided graph isomorphism learning: A multi-task framework for fault diagnosis and remaining useful life prediction","authors":"Junyu Qi , Zhuyun Chen , Yun Kong , Wu Qin , Yi Qin","doi":"10.1016/j.ress.2025.111209","DOIUrl":"10.1016/j.ress.2025.111209","url":null,"abstract":"<div><div>Intelligent fault diagnosis and remaining useful life (RUL) prediction are essential for the reliable operation of rotating machinery. These technologies enhance safety, availability, and productivity in the manufacturing industry. Graph Convolutional Networks (GCNs), an extension of deep learning (DL) to graph data, provide superior performance due to their unique data representation capabilities. Traditional condition monitoring (CM) typically requires separate models for fault diagnosis and RUL prediction, leading to challenges such as ineffective knowledge sharing and high costs associated with preparing and deploying DL models. To address these issues, this study proposes a multi-task graph isomorphism network with an attention mechanism for simultaneous fault diagnosis and RUL prediction. This method considers the interrelationship between tasks, introducing both a parameter-sharing mechanism and a self-attention mechanism. Compared to traditional single-task methods, the proposed approach offers higher accuracy, greater practicality, and reduced costs of developing the model. The effectiveness of the method is validated using experimental degradation data, demonstrating its capability to address key issues in fault diagnosis and RUL prediction, exhibiting strong potential in practical applications.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"263 ","pages":"Article 111209"},"PeriodicalIF":9.4,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144135159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0