Reliability Engineering & System Safety最新文献

{"title":"Operational readiness-oriented condition-based maintenance and spare parts optimization for multi-state systems","authors":"Shihan Tan ,&nbsp;Qiwei Hu ,&nbsp;Chiming Guo ,&nbsp;Dunxiang Zhu ,&nbsp;Enzhi Dong ,&nbsp;Fang Zhang","doi":"10.1016/j.ress.2025.111367","DOIUrl":"10.1016/j.ress.2025.111367","url":null,"abstract":"<div><div>In many military scenarios, engineered systems are required to remain satisfied with operational readiness to respond to unexpected tasks. However, the degradation caused by daily usage inherently decreases the operational readiness of these systems. Condition-based maintenance is an efficient strategy that can recover the system operational readiness by restoring the system condition. On the other hand, the activities of maintenance are often constrained by spare parts ordering. Most existing research only pays attention on the daily work and ignores the requirement of operational readiness. In this paper, a novel reinforcement learning (RL) based condition-based maintenance and spare parts optimization method for multiple unit multi-state systems (MSS) is proposed, aimed at minimizing long-term cost rate considering the requirement of operational readiness and daily work. The resulting joint decision-making problem is formulated as a discrete-time discrete-state Markov decision process (MDP) and a customized architecture of value iteration algorithm embedded with a stratified sampling Monte Carlo (SSMC) method is introduced. A real case of armored vehicles in a military base is provided to prove the effectiveness of our method. From comparative experiments and sensitivity analysis of serval examples, several interesting suggestions are presented.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111367"},"PeriodicalIF":9.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365992","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":"Development and validation of a novel method to predict flame behavior in tank fires based on CFD modeling and machine learning","authors":"Zhenqi Hu ,&nbsp;Jinlong Zhao ,&nbsp;Shaohua Zhang ,&nbsp;Hanchao Ma ,&nbsp;Jianping Zhang","doi":"10.1016/j.ress.2025.111368","DOIUrl":"10.1016/j.ress.2025.111368","url":null,"abstract":"<div><div>Ensuring storage tank farm safety involves systematic engineering. Tank fire with a large ullage height is a common type of accident and poses a serious threat to tank farms due to the air restrictions by ullage height. This study investigates the impact of ullage height on flame morphology, air entrainment, and burning behaviors through experiments and computational fluid dynamics (CFD) simulations. Results showed that ullage height of the tank significantly affect burning rate, flame morphology and air entrainment. Three burning regimes were identified as ullage height changes. Experimental and simulation data were then used in a machine learning (ML) model, which combines particle swarm optimization (PSO) and back-propagation neural networks (BPNN) to predict the mass burning rate and internal flow field. The input datasets included the tank diameter, ullage height, experimental mass burning rate, and the internal flow field predicted by the CFD model. The predicted results by the ML model agree well with the experimental and numerical data. It was shown that the larger number of the training datasets, the more accurate predictions. The new model provides a fast and efficient way to predict the burning behaviors and supports risk assessment for tank fire accidents with limited experimental and numerical inputs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111368"},"PeriodicalIF":9.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144329531","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 new random vibration response analysis method for laminates: Geometric nonlinearity and uncertainty are both involved for higher consistency with reality","authors":"Yuan Liu ,&nbsp;Xuan Zhang ,&nbsp;Xibin Cao ,&nbsp;Jinsheng Guo ,&nbsp;Zhongxi Shao ,&nbsp;Qingyang Deng ,&nbsp;Pengbo Fu ,&nbsp;Yaodong Hou","doi":"10.1016/j.ress.2025.111343","DOIUrl":"10.1016/j.ress.2025.111343","url":null,"abstract":"<div><div>To enable high-precision analysis and reliable design of laminates, a novel method is proposed for solving the random vibration response while accounting for geometric nonlinearity and structural uncertainty. The power spectral densities (PSDs) of deflection, velocity, and acceleration for a SSSS plate (indicating that all edges are simply supported) are derived using statistical linearization. In particular, the number of unknowns in the displacement field model of an SSSS-2 plate (where SSSS-2 denotes a simply supported plate with a free mid-surface) is reduced from five to three compared to conventional algorithms. This simplification reduces the complexity of the nonlinear equations and significantly improves computational efficiency. Furthermore, a novel framework was proposed, featuring a multiscale feature extraction, fusion, and learning network (MFEFLN). This network consists of three multiscale feature extraction blocks, one multiscale feature concatenation block, and one high-level feature fusion block. A dedicated network system was developed to analyze the influence of the mean values and tolerance zones of uncertain structural parameters on the random vibration responses. When predicting the same number of random response PSDs, the MFEFLN-based procedure demonstrates greater efficiency than direct Monte Carlo simulation (MCS) and superior accuracy compared to BP, GAN, LSTM, 2D CNN, and ADCNN methods. This research is beneficial for the design optimization and reliability guarantee of laminated structures by providing high-precision analysis results of the dynamic performance by covering the geometric nonlinearity and uncertainty that exist in actual products.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111343"},"PeriodicalIF":9.4,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313173","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":"Optimal mission abort and selective replacement policies for multi-state systems","authors":"Xian Zhao ,&nbsp;Zuheng Lv ,&nbsp;Qingan Qiu ,&nbsp;Yaguang Wu","doi":"10.1016/j.ress.2025.111366","DOIUrl":"10.1016/j.ress.2025.111366","url":null,"abstract":"<div><div>To mitigate the failure risk in safety-critical systems, it is beneficial to implement mission abort and rescue procedures when specific malfunction conditions are identified. Existing mission abort models predominantly focus on multi-state systems with binary-state components, often operating under the assumption that all failed components will be completely replaced after each rescue operation. However, many real-world engineering systems employ multi-state components, where replacing all failed components may not be the optimal approach due to constraints on replacement resources. Therefore, the design of effective mission abort and selective replacement policies for systems with multi-state components becomes imperative. Additionally, existing models for selective replacement primarily focus on the condition of system degradation, often overlooking the progress of missions, which can lead to suboptimal maintenance decisions, as it does not account for how mission progress and system performance interact with the demand for component replacement. This paper introduces dynamic condition-based mission abort and selective replacement policies for <em>k</em>-out-of-n: <em>F</em> systems with multi-state components, which dynamically assess the condition of system components’ state and mission execution. Mission success probability and system survivability are derived by employing recursive and discretization algorithms. We develop optimization models aimed at maximizing these probabilities while minimizing expected costs associated with maintenance and replacement actions. A case study involving a cloud computing system illustrates the advantages of the proposed policies, demonstrating their effectiveness in comparison to existing alternatives.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111366"},"PeriodicalIF":9.4,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144472276","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 assurance of Machine Learning for autonomous systems","authors":"Colin Paterson,&nbsp;Richard Hawkins,&nbsp;Chiara Picardi,&nbsp;Yan Jia,&nbsp;Radu Calinescu,&nbsp;Ibrahim Habli","doi":"10.1016/j.ress.2025.111311","DOIUrl":"10.1016/j.ress.2025.111311","url":null,"abstract":"<div><div>Machine Learning (ML) components are increasingly incorporated into systems, with different degrees of autonomy, where model performance is reported as meeting, or exceeding, the capabilities of human experts. This promises to transform products and services, in diverse domains such as healthcare, transport and manufacturing, to better serve underrepresented groups, reduce costs, and increase delivery effectiveness, especially where expert resources are scarce. The greatest potential for transformative impact lies in the development of autonomous systems for safety-critical applications where their acceptance, and subsequent deployment, is reliant on establishing justified confidence in system safety. Creating a compelling safety case for ML is challenging however, particularly since the ML development lifecycle is significantly different to that employed for traditional software systems. Typically ML development involves replacing detailed software specifications with representative data sets from which models of behaviour is learnt. Indeed, ML’s strength lies in tackling problems which are challenging for traditional software development practices. This shift in development practices introduces challenges to established assurance processes which are crucial to developing the compelling safety case required for regulation and societal acceptance. In this paper we introduce the first methodology for the Assurance of Machine Learning for use in Autonomous Systems (AMLAS). The AMLAS process describes how to systematically and attractively integrate safety assurance into the development of ML components and how to generate the evidence base for explicitly justifying the acceptable safety of these components when integrated into autonomous system applications. We describe the use of AMLAS by considering how a safety case may be constructed for an object detector for use in the perception pipeline of an autonomous driving application. We further discuss how AMLAS has been applied in several domains including healthcare, automotive and aerospace as well as supporting policy and industry guidance for defence, healthcare and automotive.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111311"},"PeriodicalIF":9.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144335663","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":"Remaining useful life prediction for multi-component systems with stochastic correlation based on auxiliary particle filter","authors":"Huifang Niu ,&nbsp;Jianchao Zeng ,&nbsp;Hui Shi ,&nbsp;Xiaohong Zhang ,&nbsp;Jianyu Liang ,&nbsp;Guannan Shi","doi":"10.1016/j.ress.2025.111357","DOIUrl":"10.1016/j.ress.2025.111357","url":null,"abstract":"<div><div>The remaining useful life (RUL) prediction of a complex system requires accurate evaluation of component degradation states and a full understanding of how these states are expected to evolve. These challenges become more complicated when stochastic correlations exist between components. To address this issue, a nonlinear Wiener process degradation model is proposed, which comprehensively considers the inherent degradation of a component and the influence of related components’ degradation levels. The degradation process of each component is modeled as a nonlinear Wiener process, and the deterioration induced by other components is described by a nonlinear function. Subsequently, an online RUL prediction method is developed for multi-component systems with varying structures. Implicit degradation states and unknown parameters are jointly estimated using auxiliary particle filtering (APF) and maximum likelihood estimation (MLE) algorithms and updated in real time according to observed data. Finally, the effectiveness and practicality of the proposed method is verified through a numerical simulation experiment and case studies of an aircraft turbine engine and a gearbox system.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111357"},"PeriodicalIF":9.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339014","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":"Enhancing procedure quality: Advanced language tools for identifying ambiguity and high-potential violation triggers","authors":"Karl Johnson ,&nbsp;Caroline Morais ,&nbsp;Edoardo Patelli","doi":"10.1016/j.ress.2025.111308","DOIUrl":"10.1016/j.ress.2025.111308","url":null,"abstract":"<div><div>In high-risk industrial environments, the clarity and accuracy of Standard Operating Procedures (SOPs) are critical for ensuring safety and regulatory compliance. The presence of ambiguities in SOPs can lead to misunderstandings, errors, and increased risks. While violations of procedural directives can significantly contribute to catastrophic outcomes. This study introduces the development of sophisticated tools utilizing both rule-based and machine learning methodologies in Natural Language Processing (NLP) specifically designed to detect ambiguities and identify high-risk steps prone to non-malevolent violation in procedural documentation. By addressing these linguistic and procedural discrepancies, we aim to enhance the clarity and applicability of SOPs, ultimately improving adherence and reducing risks in complex operational settings. The tools leverages a blend of linguistic rules to systematically identify and categorize ambiguities, and machine learning techniques with historical data to identify procedural directives with high-risk potential when violated. This enhances the precision and practical application of SOPs in sectors such as nuclear, oil and gas, and chemical processing. Initial tests demonstrate the tools’ effectiveness and promising applicability. This approach not only aids in refining SOPs but also contributes to the broader objective of enhancing operational safety and efficiency. The research underscores the importance of integrating advanced NLP techniques with traditional safety management practices to address the inherent challenges of procedural documentation in complex industrial settings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111308"},"PeriodicalIF":9.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321900","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":"iCE-NGM: Improved cross-entropy importance sampling with non-parametric adaptive Gaussian mixtures and budget-informed stopping criterion","authors":"Tianyu Zhang,&nbsp;Jize Zhang","doi":"10.1016/j.ress.2025.111322","DOIUrl":"10.1016/j.ress.2025.111322","url":null,"abstract":"<div><div>Estimating the failure probability is an essential task in engineering reliability analysis, which can be challenging for applications featuring small failure probabilities and complex numerical models. Cross entropy (CE) importance sampling is a promising strategy to enhance the estimation efficiency, by searching for the proper proposal density that resembles the theoretically optimal choice. This paper introduces iCE-NGM, an approach that enriches the recently proposed improved cross entropy (iCE) method by a non-parametric adaptive Gaussian mixture model and a budget-informed stopping criterion. An over-parameterized Gaussian mixture model will be identified with a kernel density estimation-inspired initialization and a constrained Expectation–Maximization fitting procedure. A novel budget-informed stopping criterion quantitatively balances between further refining proposal and reserving computational budget for final evaluation. A set of numerical examples demonstrate that the proposed approach performs consistently better than the classical distribution families and the existing stopping criteria.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111322"},"PeriodicalIF":9.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298034","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":"Joint optimization of multi-stage work intensity selecting and maintenance policy for a two-dimensional balanced system","authors":"Siqi Wang ,&nbsp;Songqi Li ,&nbsp;Xian Zhao ,&nbsp;Congshan Wu","doi":"10.1016/j.ress.2025.111362","DOIUrl":"10.1016/j.ress.2025.111362","url":null,"abstract":"<div><div>Most existing research on balanced systems based on component state only considers the one-dimensional linear structure, but many engineering systems are two-dimensional. Driven by this reality, a two-dimensional balanced system reliability model is proposed. All components are arranged in a two-dimensional matrix, and divided into grids of equal size. The system is required to complete a task containing several phases. The system has several optional work intensities such that it can complete more tasks under a higher work intensity, but the failure rate also increases. When the maximum difference of component states in each grid is less than a predetermined threshold, the system is balanced. When the system is unbalanced, or the total number of failed components in a grid exceeds a limit, it fails. To complete more tasks and reduce the loss caused by system failure, a joint policy of work intensity selecting and maintenance is proposed. A Markov decision process is used to describe the system operation process. The optimal results are calculated by the value iteration algorithm. Finally, a two-dimensional manufacturing system is taken as an example to verify the effectiveness of the proposed joint strategy.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111362"},"PeriodicalIF":9.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365418","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 modified TimeGAN-based data augmentation approach for the state of health prediction of Lithium-Ion Batteries","authors":"Soufian Echabarri ,&nbsp;Phuc Do ,&nbsp;Hai-Canh Vu ,&nbsp;Pierre-Yves Liegeois","doi":"10.1016/j.ress.2025.111297","DOIUrl":"10.1016/j.ress.2025.111297","url":null,"abstract":"<div><div>Lithium-ion batteries are critical components of zero-emission electro-hydrogen generators (GEH2), where accurate performance prediction is essential for ensuring optimal operation and enabling effective predictive maintenance. Data-driven models have become increasingly prominent for predicting the State of Health (SOH) of lithium-ion batteries due to their high accuracy and reduced development time. However, in hybrid systems like GEH2, where the battery frequently remains inactive while the fuel cell supplies most of the power, the available battery data is limited. This data scarcity presents a significant challenge for achieving accurate SOH prediction. To address this challenge, we propose a novel data augmentation approach that integrates Time-series Generative Adversarial Network with a Transformer and a Gated Recurrent Unit to enhance data availability and improve prediction accuracy. This new approach enhances the model’s ability to capture long-term temporal dependencies within multivariate battery parameters while effectively addressing irregular time intervals, a common challenge in real-world batteries datasets. We evaluated the proposed approach using real-world industrial datasets from four distinct GEH2 batteries and two additional batteries from the publicly available NASA dataset. The performance of SOH prediction was assessed using a Long Short-Term Memory (LSTM) model trained on augmented data generated by various data augmentation techniques. The results consistently demonstrate that our approach outperforms all competing methods, highlighting its superior ability to enhance data for lithium-ion batteries. These findings highlight the effectiveness of our approach in enhancing predictive accuracy and robustness, making it highly suitable for real-world battery applications.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"264 ","pages":"Article 111297"},"PeriodicalIF":9.4,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298033","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}