Reliability Engineering & System Safety最新文献

{"title":"Reliability analysis of consecutive -ks-outs-of-ns: F system with a circular polygon structure considering subsystems balance in shock environment","authors":"Chen Fang ,&nbsp;Chenhao Zeng ,&nbsp;Jiaran Li ,&nbsp;Jianhui Chen","doi":"10.1016/j.ress.2025.111015","DOIUrl":"10.1016/j.ress.2025.111015","url":null,"abstract":"<div><div>We develop a reliability model for a consecutive-<span><math><msub><mi>k</mi><mi>s</mi></msub></math></span>-out-of-<span><math><msub><mi>n</mi><mi>s</mi></msub></math></span>:<span><math><mi>F</mi></math></span> system characterized by a circular polygon structure operating in a shock environment, which is modeled as a homogeneous absorbing Markov process. This model enhances traditional system structures, making it more applicable to real-world engineering scenarios. Such systems are commonly found in applications like drone swarms, data transmission, and communication networks. Specifically, three types of random external shocks are considered, component failures may occur when an extreme shock arrives, or when the number of effective shocks reaches a fixed value. The balance of subsystems is assessed based on the operational states of all components within each subsystem. To estimate the corresponding state probability functions and other reliability metrics, we employ a two-step finite Markov chain imbedding approach along with phase-type distributions. A Monte-Carlo simulation algorithm to obtain the first failure time of the system. Finally, we present a numerical example involving drone swarms to demonstrate the practical application and effectiveness of the proposed model.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111015"},"PeriodicalIF":9.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703896","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 solid-state lithium batteries based on spatial–temporal relations and neuronal ODE-assisted KAN","authors":"Zhenxi Wang ,&nbsp;Yan Ma ,&nbsp;Jinwu Gao ,&nbsp;Hong Chen","doi":"10.1016/j.ress.2025.111003","DOIUrl":"10.1016/j.ress.2025.111003","url":null,"abstract":"<div><div>Remaining useful life prediction (RUL) of solid-state lithium batteries (SSLIBs) can accelerate the maintenance and optimization process, facing challenges in insufficient exploration of implicit degradation information, complexity of computational costs and poor interpretability. To address these issues, a novel method for obtaining comprehensive implicit information during the degradation process is proposed. Firstly, topological relations are introduced by using graph attention network (GAT) to comprehensively represent the implicit relations among external parameters. It is utilized to supplement the interdependencies between physical measurements of multiple health indicators for SSLIBs, avoiding manual feature engineering. Then, a neural ordinary differential equation (ODE) composed of Kolmogorov–Arnold network (KAN) is developed to capture the continuous dynamic implicit state trajectories during the degradation process, overcoming the issue of ignoring dynamic variations for implicit relations in external parameters. Moreover, KAN is adopt as a regressor, which ensures the interpretability of the constructed RUL prediction model for SSLIBs while reducing the computational cost. The comparison analysis in the real SSLIBs degradation datasets demonstrate the optimal minimum root mean square errors and the parameters of the model are reduced by 39.03% and 49.13%, respectively. It also indicates that the proposed method can provide new perspectives and solutions for RUL prediction of SSLIBs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111003"},"PeriodicalIF":9.4,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632103","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":"Reliability analysis and optimization of multi-state tree-structured systems with performance sharing mechanism","authors":"Liudong Gu ,&nbsp;Guanjun Wang ,&nbsp;Yifan Zhou","doi":"10.1016/j.ress.2025.110990","DOIUrl":"10.1016/j.ress.2025.110990","url":null,"abstract":"<div><div>Existing studies on reliability modeling of performance sharing systems (PSSs) have primarily focused on common bus or series structure. However, in some practical PSSs, units are organized in a tree structure. This paper addresses the research gap in reliability optimization of tree-structured PSSs. In such systems, units with random performance and demand are arranged in different layers. The surplus performance of each unit can be shared by the connected units located in adjacent layers. The system fails if there exists performance deficiency. A recursive method is proposed to determine the state of connected units. Additionally, a reliability evaluation algorithm is developed based on universal generating function. We further investigate the optimization of transmission capacity allocation to maximize system reliability. To streamline the search for the optimal solution, a strategy space reduction approach is introduced to derive the more appropriate value range of each decision variables, thereby simplifying the optimization process. Genetic algorithm (GA) is employed to identify the optimal solution within the optimized strategy space. Validation through two power systems demonstrates that the proposed reliability evaluation method accurately evaluates the system reliability, and the improved GA efficiently finds a superior transmission capacity allocation strategy compared to the conventional GA.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110990"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619491","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":"Learning non-stationary model of prediction errors with hierarchical Bayesian modeling","authors":"Menghao Ping ,&nbsp;Wang-Ji Yan ,&nbsp;Xinyu Jia ,&nbsp;Costas Papadimitriou ,&nbsp;Ka-Veng Yuen","doi":"10.1016/j.ress.2025.111012","DOIUrl":"10.1016/j.ress.2025.111012","url":null,"abstract":"<div><div>The hierarchical Bayesian modeling (HBM) framework has proven its effectiveness in addressing the model updating problem. However, the assumption of Gaussian white noise for prediction errors in HBM overlooks their inherent non-stationary uncertainties, which are prevalent in engineering applications. Ignoring the non-stationaries of prediction errors can lead to significant errors in identifying model parameters, ultimately resulting in biased predictions and reduced reliability of the updated model. To comprehensively estimate the non-stationary uncertainties of prediction errors while simultaneously identifying unknown physical model parameters, a new HBM framework is proposed, wherein the prediction errors are modeled using a non-stationary Gaussian process (GP). In this framework, the hyper parameters consist of two sets: one representing the statistics of the GP model and the other representing the distribution parameters of the physical model parameters. Due to the complexity stemming from the large number of parameters required in the non-stationary GP model, directly inferring the joint posterior distribution of all the hyperparameters is computationally infeasible. To address this issue, a sequential process is designed to infer the marginal distribution of each set of parameters individually. The product of these two marginal distributions is then used as an approximation of the joint distribution. Furthermore, an iterative procedure is proposed to ensure the consistency between the two distributions, ultimately achieving the optimal approximation of the joint posterior distribution. The effectiveness of the proposed framework is validated by identifying the structural parameters and prediction errors of time-history responses in a structural dynamic example using simulated data. It is then successfully applied to identify the fatigue crack growth (FCG) model using experimental data, resulting in improved predictive accuracy, as evidenced by the significantly narrower predicted interval for FCG life compared to the prediction made by the HBM.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111012"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143683515","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":"Reliability modeling for three-version machine learning systems through Bayesian networks","authors":"Qiang Wen,&nbsp;Fumio Machida","doi":"10.1016/j.ress.2025.111016","DOIUrl":"10.1016/j.ress.2025.111016","url":null,"abstract":"<div><div>Machine learning (ML) is extensively employed in AI-powered systems including safety-critical applications such as autonomous vehicles. The outputs from ML models are sensitive to real-world input data and error-prone, thereby improving the reliability of ML systems’ outputs has become a critical challenge in ML system design. In this paper, we introduce N-version ML architectures to enhance the ML system reliability and propose Bayesian Networks (BNs) models to evaluate the reliability of system outputs targeting three-version ML systems. The proposed BN reliability models allow us to formulate five distinct types of three-version ML architectures that are composed of diverse models and diverse input data sources. To validate the BN reliability models with real samples from ML systems, we conduct empirical studies on traffic sign recognition tasks and evaluate prediction performance. As a result, we find the prediction residuals between the observed reliability and the predicted reliability by the BN reliability models are less than 0.015 across all data sets, which is much better than the prediction performance by the baseline model. In addition, in comparison to the previous reliability models without exploiting BNs, the proposed models exhibit an advantage in reliability prediction, except for the triple model with single input architecture.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111016"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143703935","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":"Integrating causal representations with domain adaptation for fault diagnosis","authors":"Ming Jiang ,&nbsp;Kuang Zhou ,&nbsp;Jiahui Gao ,&nbsp;Fode Zhang","doi":"10.1016/j.ress.2025.110999","DOIUrl":"10.1016/j.ress.2025.110999","url":null,"abstract":"<div><div>In practical fault diagnosis, obtaining sufficient samples is often challenging. Transfer learning can help by using data from related domains, but significant distribution differences often exist due to different working conditions. To address this issue, cross-domain fault diagnosis (CDFD) has attracted increasing attention. However, most CDFD methods rely on statistical dependencies, which restricts their ability to uncover intrinsic mechanisms and affects both performance and reliability. In this paper, a Cross-domain Fault Diagnosis model based on Causal Representation learning (CFDCR) is proposed. This method employs causal representation learning with a graph autoencoder to learn invariant representations across domains, thereby improving the robustness of the prediction model. It further employs domain adversarial networks to align feature distributions, thus mitigating conditional distribution disparities between source domain data and target fault data, ultimately enhancing model performance. Experimental results on various bearing fault datasets demonstrate that the proposed cross-domain fault diagnosis model can effectively utilize related source domain data to guide fault classification tasks in the target domain and achieve more robust fault predictions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110999"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143619492","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":"Multiple classifiers inconsistency-based deep adversarial domain generalization method for cross-condition fault diagnosis in rotating systems","authors":"Lei Gao ,&nbsp;Qinhe Gao ,&nbsp;Zhihao Liu ,&nbsp;Hongjie Cheng ,&nbsp;Jianyong Yao ,&nbsp;Xiaoli Zhao ,&nbsp;Sixiang Jia","doi":"10.1016/j.ress.2025.111017","DOIUrl":"10.1016/j.ress.2025.111017","url":null,"abstract":"<div><div>Unknown fault operating conditions and the absence of fault data pose significant challenges for real-time fault diagnosis, as the generalization capability of models is heavily reliant on transferable knowledge from a single operating condition. To overcome these limitations, a novel deep adversarial domain generalization framework based on multiple classifiers inconsistency (DADG-MCI) is designed to improve generalized ability without the need for target domain data during training. Initially, unique features of the multiple source domains are captured through the probability output inconsistency of the multiple domain-specific classifiers. Subsequently, adversarial training facilitates finer-grained global feature alignment across multiple source domains, which ensures that the extracted deep features possess strong generalization capabilities. Most importantly, DADG-MCI introduces the multiple classifiers inconsistency to measure multi-domain distributional discrepancy based on Wasserstein distance, which captures feature distribution differences between domains through joint optimization of the multi-classifier module. Finally, two challenging rotating machinery fault datasets are used to evaluate the performance of DADG-MCI for cross-condition fault diagnosis. Compared to several state-of-the-art methods, DADG-MCI achieves the highest average diagnostic accuracies and successfully applies to unseen operating conditions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111017"},"PeriodicalIF":9.4,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143628420","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":"Suggestion of specific performance shaping factor update for the human reliability analysis framework of the TRIGA research reactor","authors":"Wasin Vechgama ,&nbsp;Jinkyun Park ,&nbsp;Yochan Kim ,&nbsp;Saensuk Wetchagarun ,&nbsp;Anantachai Pechrak ,&nbsp;Weerawat Pornroongruengchok ,&nbsp;Kampanart Silva","doi":"10.1016/j.ress.2025.111010","DOIUrl":"10.1016/j.ress.2025.111010","url":null,"abstract":"<div><div>Based on the human reliability analysis (HRA) framework of the TRIGA research reactor, performance shaping factor (PSF) estimation is an important step when considering the effects of specific operating or working cultures in determining human error probabilities (HEPs). This study aims to suggest a method to develop specific PSFs for the HRA framework of the TRIGA research reactor through a TRR-1/M1 case study. The PSF survey of the HRA framework was developed based on the EMBRACE method to consider the negative impacts of each PSF compared to the normal situation of all four cognitive activities in the errors of omission and errors of commission modes. Given the varied experiences of experts, expert elicitation was employed to categorize high-performing, low-performing, and informative experts to ensure reliable data for PSF analysis. For low-performing and informative experts, the survey results of PSFs were improved by additional surveys to support an appropriate dataset for analyzing the PSFs of the HRA framework. The impact of PSFs within the HRA framework was estimated using the updated normal distribution of the posterior PSFs based on the classical model. The success likelihood index method successfully integrated all subjective expert judgments into a cohesive representation and offered a better systematic consensus model to generalize HEPs in the form of a normal distribution based on a large group of experts.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111010"},"PeriodicalIF":9.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143601840","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":"Are the processing facilities safe and secured against cyber threats?","authors":"Rajeevan Arunthavanathan ,&nbsp;Faisal Khan ,&nbsp;Zaman Sajid ,&nbsp;Md. Tanjin Amin ,&nbsp;Kalyan Raj Kota ,&nbsp;Shreyas Kumar","doi":"10.1016/j.ress.2025.111011","DOIUrl":"10.1016/j.ress.2025.111011","url":null,"abstract":"<div><div>Most processing facilities, including those in the chemical, petrochemical, and mineral industries, aim to operate as cyber-physical systems to achieve higher plant efficiency, productivity, and, in some cases, safety. However, this digital transformation increases the vulnerability of process control systems to cyber-attacks, which can disrupt operations and lead to catastrophic consequences. Traditional approaches often consider cybersecurity solely as an Information Technology (IT) issue, overlooking the critical role of Operational Technology (OT) in managing cyber threats and ensuring plant resilience. This article reviews OT cybersecurity challenges and solutions, culminating in developing a robust OT-specific cybersecurity framework. The proposed framework integrates threat modeling, real-time attack detection, and real-time mitigation to protect physical plant operations while ensuring operational continuity. Unlike existing models, the proposed framework bridges the safety-security gap by combining IT-driven cybersecurity strategies with OT-specific risk management and defense mechanisms. Key features of the framework include layered defense mechanisms, adaptive response strategies, and risk-based prioritization, all of which collectively strengthen resilience against advanced cyber threats. By systematically reviewing current cybersecurity practices and proposing a comprehensive framework, this study further recommends approaches to enhance scalability and practical applicability for advancing cybersecurity in process plant operations. The findings underscore the necessity of integrating IT and OT cybersecurity strategies to ensure industrial safety, security, and uninterrupted operations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111011"},"PeriodicalIF":9.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143592802","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":"Dynamic reliability evaluation considering the stochastic evolving process based on extreme characteristics of system responses","authors":"Di Zhou ,&nbsp;Zhen Chen ,&nbsp;Zhaoxiang Chen ,&nbsp;Jinrui Han ,&nbsp;Ershun Pan","doi":"10.1016/j.ress.2025.111005","DOIUrl":"10.1016/j.ress.2025.111005","url":null,"abstract":"<div><div>The randomness of high-frequency system responses in engineering, such as vibration, stress, and displacement, poses a significant challenge to system reliability and can potentially lead to system failure. This study proposes a novel stochastic evolving process that directly incorporates random extreme values and their occurrence times, enabling the characterization of the spatial distribution and temporal evolution of dynamic system responses. By integrating the saddle-point approximation and renewal process, the proposed approach effectively captures the statistical properties and variation patterns of extreme responses. Additionally, a convolution technique is explored to handle both known and unknown process parameters. A general reliability model is formulated with rigorous theoretical reasoning to assess dynamic system performance. The unified probabilistic framework is developed that systematically integrates dynamic response evolution and different random characteristics for reliability evaluation in stochastic environments. Specifically, an analytical approach is developed for systems with memoryless properties, while a general numerical method, based on the Laplace transform, is introduced to evaluate equipment reliability under stochastic conditions in both the complex frequency and time domains. The proposed method is validated through three engineering case studies, analyzing the impact of mean values and standard deviations on system reliability. The results demonstrate strong consistency and accuracy, aligning well with Monte Carlo simulations, thereby confirming the validity and practical applicability of the approach.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111005"},"PeriodicalIF":9.4,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143610621","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}