Reliability Engineering & System Safety最新文献

{"title":"SDCGAN: A CycleGAN-based single-domain generalization method for mechanical fault diagnosis","authors":"Yu Guo ,&nbsp;Xiangyu Li ,&nbsp;Jundong Zhang ,&nbsp;Ziyi Cheng","doi":"10.1016/j.ress.2025.110854","DOIUrl":"10.1016/j.ress.2025.110854","url":null,"abstract":"<div><div>In recent years, fault diagnosis based on domain generalization has attracted increasing attention as an effective approach to address the challenge of domain shift. most existing approaches depend on learning domain-invariant representations from multiple source domains, limiting their practical application in fault diagnosis. To address this issue, this paper introduces a single-domain generalization method for mechanical fault diagnosis, the Single-Domain Cycle Generative Adversarial Network (SDCGAN). A CycleGAN-based domain generation module is introduced to produce extended domains that exhibit substantial divergence from the source domain, enhancing the model's generalization capability. The diagnostic task module subsequently extracts domain-invariant features from both the source and extended domains. Furthermore, an adversarial contrastive training strategy is employed to learn generalized features robust to unknown domain shifts. Comprehensive experiments on two mechanical datasets verify the effectiveness of the proposed method, while ablation studies validate the contributions of its components, highlighting its potential for real-world applications.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"258 ","pages":"Article 110854"},"PeriodicalIF":9.4,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143387582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrating physical knowledge for generative-based zero-shot learning models in process fault diagnosis","authors":"Guoqing Mu ,&nbsp;Ching-Lien Liu ,&nbsp;Junghui Chen","doi":"10.1016/j.ress.2025.110852","DOIUrl":"10.1016/j.ress.2025.110852","url":null,"abstract":"<div><div>Despite longstanding operational processes, persistent undiagnosed issues and inefficiencies continue to exist. Specifically, the collected data with specified faults is often either non-existent or sparse. The challenge lies in the absence of specified faults for reliable training of fault diagnosis models in such processes. This study proposes an innovative physical knowledge-guided zero-sample fault diagnosis method, which decomposes process variables into states and defines attributes based on domain knowledge. This transformation of variables into the attribute space replaces the traditional label space. The method involves three key steps: (1) Constructing the Seen Fault Latent Space: Utilizing seen fault data through the Conditional Variational Auto-Encoder model and Linear Discriminant Analysis in the latent space to classify seen faults. (2) Extending the Model Space with Unseen Fault Attributes: Using attributes to extend the unknown fault space and introducing a discriminator to ensure accurate separation of seen and unseen faults. (3) Retraining the Model: Using the data generated in the second step to retrain the model, enabling the diagnosis of both seen and unseen faults by the encoder. Experiments on numerical, continuous stirred tank reactor, and three-level tank examples demonstrate a significant 11 % improvement in classification accuracy for unseen fault samples compared to traditional methods.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"258 ","pages":"Article 110852"},"PeriodicalIF":9.4,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143379304","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}

{"title":"Nataf-based probabilistic buffeting prediction of overhead transmission lines under multi-dimensional turbulent wind excitation","authors":"Wen-Long Du ,&nbsp;Xing Fu ,&nbsp;Deng-Jie Zhu ,&nbsp;Gang Li ,&nbsp;Hong-Nan Li ,&nbsp;Zeng-Hao Huang ,&nbsp;Ying-Zhou Liu","doi":"10.1016/j.ress.2025.110846","DOIUrl":"10.1016/j.ress.2025.110846","url":null,"abstract":"<div><div>The overhead transmission line (OTL) is a complex nonlinear system susceptible to turbulence-induced buffeting. Classical one-dimensional buffeting analysis overlooks potential vertical turbulence. Additionally, the turbulence parameters associated with stochastic wind fields are simply assumed to be deterministic. Such simplified schemes might cause severe misestimation of extreme responses. Therefore, this paper presents a Nataf-based probabilistic buffeting prediction framework of OTLs under multi-dimensional turbulent wind. Initially, a two-dimensional influence line method is introduced to replace the time-consuming nonlinear finite element analysis (NFEA). Subsequently, Nataf transformation is employed to generate turbulence parameter samples, effectively preserving the marginal probability distribution and correlation structure. Then, the three-dimensional wind speeds are simultaneously synthesized, and the impact of turbulence parameter uncertainty on wind field characteristics is investigated. Next, the time-frequency buffeting responses are analytically estimated, and a quantile-based approach is proposed to quantify the uncertainty of extreme buffeting responses. Finally, the stochastic sensitivity analyses are performed to determine the relative sensitivity of various responses to each turbulence parameter. The results demonstrate that the two-dimensional influence line method exhibits significant efficiency advantages; All deterministic extreme responses are less than the 0.5 quantile of uncertain results; The top three turbulence parameters in sensitivity ranking are identical under different conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110846"},"PeriodicalIF":9.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219616","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}

{"title":"Designing random collaborative warranty and customizing maintenance strategies for systems subject to mission cycles","authors":"Lijun Shang ,&nbsp;Baoliang Liu ,&nbsp;Rui Peng","doi":"10.1016/j.ress.2025.110843","DOIUrl":"10.1016/j.ress.2025.110843","url":null,"abstract":"<div><div>Existing warranties are made by manufacturers subjectively and unilaterally with the aims of cutting costs, increasing sales volumes, and so on. However, they fail to meet users' individual needs such as effectively managing reliability or ensuring higher and stable production capacities. In response to this situation, the current paper puts forward random collaborative warranty models subject to mission cycles. In these models, manufacturers and users jointly set terms to fit users' actual needs. These warranties not only fulfill personalized needs but also function as value-added services, thus creating profit-making opportunities for manufacturers. Moreover, aiming to effectively manage the post-warranty reliability of systems, the paper devises post-warranty maintenance strategies such as trivariate random replacement with preventive maintenance and bivariate random combination replacement. The strategies' terms are based on different usage rate scenarios of the post-warranty system, which are determined by whether limited missions are completed before a specific time. The proposed solutions are modeled quantitatively, and numerical analyses are conducted on some typical aspects to uncover the underlying mechanisms and derive valuable insights. One notable finding is that users can achieve lower repair costs if they actively apply the two-scale approach following the 'whichever expires last' principle.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110843"},"PeriodicalIF":9.4,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219614","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}

{"title":"A variational Bayesian deep reinforcement learning approach for resilient post-disruption recovery of distribution grids","authors":"Zhaoyuan Yin ,&nbsp;Chao Fang ,&nbsp;Yiping Fang ,&nbsp;Min Xie","doi":"10.1016/j.ress.2025.110840","DOIUrl":"10.1016/j.ress.2025.110840","url":null,"abstract":"<div><div>The increasing frequency of natural hazards and the resulting disruptions in distribution systems emphasize the urgent need for resilient post-disruption recovery. Due to high computational complexities in solving large-scale models, traditional optimization methods face significant challenges in emergency responses for grid recovery planning. Deep reinforcement learning (DRL) is emerging as a promising alternative for tackling combinatorial problems but struggles with uncertainty quantification and the curse of dimensionality. To address these issues and promote resilient post-disruption restoration, this study proposes a variational Bayesian DRL approach for developing optimal repair strategies after system disruptions. Recovery planning is framed as a Markov Decision Process, integrating data-driven environmental interactions and optimization-based reward evaluation. The Bayesian deep Q-network, using stochastic variational inference, quantifies the inherent uncertainties of component repair times due to varying repair resources and environmental stochasticity. A novel variational Bayesian dueling double deep Q-network is designed to mitigate the challenges in estimating Q-values within large state and action spaces. Case studies of real-world emergencies in the Hong Kong region show that the proposed methodology is effective and robust compared to several alternative approaches. The analysis on numerical results provides valuable insights for emergency restoration.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110840"},"PeriodicalIF":9.4,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219610","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}

{"title":"Resource allocation approaches for improving safety and operations at level crossings: State of the art, existing challenges, and future research needs","authors":"Payam Afkhami ,&nbsp;Razieh Khayamim ,&nbsp;Bokang Li ,&nbsp;Marta Borowska-Stefańska ,&nbsp;Szymon Wiśniewski ,&nbsp;Amir M. Fathollahi-Fard ,&nbsp;Yui-yip Lau ,&nbsp;Maxim A. Dulebenets","doi":"10.1016/j.ress.2025.110839","DOIUrl":"10.1016/j.ress.2025.110839","url":null,"abstract":"<div><div>Level crossings (LCs) present substantial safety challenges due to their unique role as intersections where road and rail traffic meet at the same elevation. This study provides a comprehensive review and analysis of resource allocation studies aimed at enhancing safety and operational efficiency at LC locations. The collected studies are categorized into three main groups, including resource allocation for countermeasure implementation, resource allocation for crossing closures, and resource allocation for constructing grade separations. Each group of studies is systematically reviewed focusing on the evaluated fields, countermeasure considerations, sustainability considerations, sustainability dimensions, methodologies used, and case studies. By synthesizing the current literature, this study highlights the strengths and weaknesses of various strategies, emphasizing the need for a holistic framework that integrates safety, economic, and environmental sustainability dimensions. The key contributions include a detailed evaluation of mathematical optimization models for selecting LCs for resource allocation, consideration of important practical factors associated with resource allocation decisions, safety enhancement costs, collision risk levels, and community engagement. The findings suggest that safety performance, design, and upgrades are the most common fields evaluated by the existing studies. A substantial number of studies on LC closures and grade separations also capture traffic delays in decision making. Moreover, essential future research directions for countermeasure implementation, crossing closures, and grade separations are presented as well. This study can serve as a valuable point of reference for researchers and practitioners in the field of LC safety and operational enhancement by consolidating diverse findings and methodologies from the existing research.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110839"},"PeriodicalIF":9.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219611","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}

{"title":"A hybrid physics informed predictive scheme for predicting low-cycle fatigue life and reliability of aerospace materials under multiaxial loading conditions","authors":"Butong Li,&nbsp;Junjie Zhu,&nbsp;Xufeng Zhao","doi":"10.1016/j.ress.2025.110838","DOIUrl":"10.1016/j.ress.2025.110838","url":null,"abstract":"<div><div>Engineering components such as engine blades, turbofans, external parts, etc., are often subjected to complex loads in the serving environment. Fatigue failure of components under multiaxial loading will occur, causing a severe influence on operational safety. Centered on low-cycle fatigue under multiaxial loading conditions, we have developed a novel fatigue life prediction framework, which utilizes the physics-guided machine learning approach as a surrogate model for fatigue life prediction. We conducted preliminary experiments to obtain the material's mechanical properties and established reliable finite element analysis (FEA) models based on these properties. Subsequently, we generated high-confidence datasets using the FEA models. By leveraging the strengths of both deep learning methods and LightGBM, we proposed a fusion surrogate model called DL-LGBM-DRS. The DL-LGBM-DRS can efficiently and accurately predict low-cycle fatigue life under various multiaxial loading conditions. Lastly, we defined a new fatigue life degradation relationship, KBM-N, using Brown-Miller parameters and fitted probabilistic fatigue life degradation curves based on the KBM-N relations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110838"},"PeriodicalIF":9.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219618","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}

{"title":"Safety risk assessment for connected and automated vehicles: Integrating FTA and CM-improved AHP","authors":"Xiangyu Zheng ,&nbsp;Qi Liu ,&nbsp;Yufeng Li ,&nbsp;Bo Wang ,&nbsp;Wutao Qin","doi":"10.1016/j.ress.2025.110822","DOIUrl":"10.1016/j.ress.2025.110822","url":null,"abstract":"<div><div>To reduce safety accidents from functional failures in connected and automated vehicles (CAVs), risk assessment and prevention are essential. However, traditional hazard analysis and risk assessment (HARA) methods suffer from limitations: insufficient quantitative assessment and inadequate consideration of ambiguity and uncertainty. To this end, we propose a quantitative risk assessment method for CAVs based on fault tree analysis (FTA) and cloud model (CM)-improved analytic hierarchy process (AHP). First, we use the golden section method and CM to refine the automotive safety integrity level (ASIL), representing ambiguity in level boundaries. Next, we incorporate potential functional failure paths and construct basic events for the FTA based on the failure modes of vehicle components. Meanwhile, the CM-improved AHP is applied to assess risks for each basic event, reducing uncertainty from subjective data. Finally, we combine the technique for order preference by similarity to an ideal solution (TOPSIS) with the conversion function to provide quantitative probabilities for the top event and perform a sensitivity analysis of basic events. A case study on a real open-source test vehicle shows that the proposed method quantifies the probability of automatic emergency braking (AEB) system failure and ranks the risk for each basic event. Compared to existing methods, it has significant advantages in the comprehensiveness and objectivity of risk assessment, providing more accurate information for risk prevention.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110822"},"PeriodicalIF":9.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219609","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}

{"title":"Multi-agent-based failure modeling for uncrewed swarm systems considering cross-layer diffusion characteristics","authors":"Xing Guo ,&nbsp;Qiang Feng ,&nbsp;Zeyu Wu ,&nbsp;Meng Liu ,&nbsp;Yi Ren ,&nbsp;Chao Yang ,&nbsp;Zili Wang","doi":"10.1016/j.ress.2025.110831","DOIUrl":"10.1016/j.ress.2025.110831","url":null,"abstract":"<div><div>Uncrewed swarm systems (USSs) are injecting renewed vigor into societal development and economic growth, and their failure modeling is crucial for ensuring the safety and stability of system operations. However, traditional modeling methods are insufficient for describing the cross-layer diffusion characteristics of USS failure. In this context, proposed here is a multi-agent-based failure modeling approach to establish the groundwork for analyzing USS failure. A failure modeling framework grounded in agent-based modeling methodology is developed to efficiently capture and organize the multi-layer failure information of a USS. Expanding on this framework, the interaction mechanism of agents is designed to delineate the operational processes and failure propagation paths of the USS. A dynamic clock failure model, structural functions, and functional constraint matrices are integrated effectively to describe the cross-layer failure behaviors of the components, subsystems, and nodes of the USS. Furthermore, a unified modeling approach is proposed to describe the failure propagation within and between nodes. Finally, a case study of a USS comprising 16 uncrewed aerial vehicles and eight satellites is conducted to validate the effectiveness of the proposed approach. The simulation results reveal that the electric distribution board, voltage stabilizer, and flight control board significantly influence the mission completion.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110831"},"PeriodicalIF":9.4,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143286320","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}

{"title":"A systematic procedure for the analysis of maintenance reports based on a taxonomy and BERT attention mechanism","authors":"Dario Valcamonico ,&nbsp;Piero Baraldi ,&nbsp;July Bias Macêdo ,&nbsp;Márcio Das Chagas Moura ,&nbsp;Jonathan Brown ,&nbsp;Stéphane Gauthier ,&nbsp;Enrico Zio","doi":"10.1016/j.ress.2025.110834","DOIUrl":"10.1016/j.ress.2025.110834","url":null,"abstract":"<div><div>This work proposes a systematic procedure for analyzing maintenance reports to support maintenance decision-making for a fleet of similar systems. The proposed procedure allows achieving three objectives: (1) grouping maintenance interventions, (2) identifying common characteristics in the maintenance interventions, and (3) recognizing occurrences of rare events of maintenance intervention. Specifically, the attention mechanism of Bidirectional Encoder Representation from Transformer (BERT) and the Density Based Spatial Clustering Applications with Noise (DBSCAN) methods are combined to group maintenance interventions according to their similarity of stated features. A taxonomy of the words used in the textual reports to state the maintenance interventions is developed to systematically identify common features of the clusters, such as the involved components, their working state, the occurred failures or malfunctions, the performed maintenance actions and the personnel that has performed the intervention. The proposed procedure is applied to a repository of reports of maintenance interventions performed on mechanical and electric components of traction systems of a fleet of trains. The obtained results show that it can effectively support decision-making on the maintenance of traction systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"257 ","pages":"Article 110834"},"PeriodicalIF":9.4,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143219608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}