Reliability Engineering & System Safety最新文献_第8页

Multi-dimensional sequence embedding and improved Informer for prediction of industrial alarm events 面向工业报警事件预测的多维序列嵌入和改进的Informer

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-29 DOI: 10.1016/j.ress.2026.112317

Wenbin Jiang , Wenkai Hu , Yupeng Li , Weihua Cao

{"title":"Multi-dimensional sequence embedding and improved Informer for prediction of industrial alarm events","authors":"Wenbin Jiang , Wenkai Hu , Yupeng Li , Weihua Cao","doi":"10.1016/j.ress.2026.112317","DOIUrl":"10.1016/j.ress.2026.112317","url":null,"abstract":"<div><div>As an effective alarm monitoring strategy, alarm event prediction helps mitigate the impact of alarm floods and the risk of industrial accidents by providing early warnings of potential future alarms, thereby allowing operators more time to take corrective action. However, in continuous industrial processes, varying operating conditions and abnormal states cause real-time fluctuations in alarm rates, posing challenges for existing methods to achieve satisfactory prediction performance. In view of such issues, this paper proposes a new alarm event prediction method adapting to variable alarm rates over long-term consecutive alarm monitoring periods using multi-dimensional sequence embedding and improved Informer. The contributions are threefold: 1) An adaptive alarm sequence segmentation strategy is designed to generate input alarm sequences adapting to alarm rates; 2) a multi-dimensional sequence embedding method based on both the alarm tags and time intervals is proposed to convert the textual alarm messages into numerical vectors; and 3) an Informer based alarm event prediction model is developed for precise and early alarm event prediction under alarm flood and non-flood periods. A case study based on the Vinyl Acetate Monomer public model is given to prove the effectiveness of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112317"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098524","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

Failure-prediction-activated Packet Rebroadcasting for Packet Loss Mitigation in Unmanned Aerial Vehicle Swarm Networks 基于故障预测激活的无人机群网络丢包重广播

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-31 DOI: 10.1016/j.ress.2026.112331

Reuben Yaw Hui Lim , Joanne Mun-Yee Lim , Boon Leong Lan , Patrick Wan Chuan Ho , Nee Shen Ho , Thomas Wei Min Ooi

{"title":"Failure-prediction-activated Packet Rebroadcasting for Packet Loss Mitigation in Unmanned Aerial Vehicle Swarm Networks","authors":"Reuben Yaw Hui Lim , Joanne Mun-Yee Lim , Boon Leong Lan , Patrick Wan Chuan Ho , Nee Shen Ho , Thomas Wei Min Ooi","doi":"10.1016/j.ress.2026.112331","DOIUrl":"10.1016/j.ress.2026.112331","url":null,"abstract":"<div><div>Communication reliability between a UAV swarm and the ground control station (GCS) is crucial for its safe deployment, but unforeseen interference can cause communication failure. There is a lack of proposals on the mitigation of such failure. Here, we propose packet-rebroadcasting by UAV (with packet-loss-count (PLC) based probability) and GCS to mitigate packet loss to maintain safety. This mitigation scheme is activated in waypoint control mode when failure is predicted using a modified throughput as proxy for reliability. The performance of our novel approach is evaluated for a representative UAVs-GCS network across UAV and MANET interference scenarios with low to high interference strengths. Our modified throughput, which correlates far better with reliability than throughput, achieved similar high mean specificity (∼1) for failure prediction, but much lower mean false negative rate overall with concomitant low failure risks. Our proposed mitigation scheme out-performed its variants (with different combinations of rebroadcasting strategy (hybrid/UAV-only) and probability (PLC/fixed/distance-based), and no rebroadcasting), achieving the lowest probability (∼2%) of three consecutive packet loss with the highest rebroadcast efficiency. Our approach enables UAV swarm deployment with a high-level of safety, even if strong interference is encountered.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112331"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175038","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

Multi-objective optimisation of arctic shipping routes considering navigational risk, voyage time, and fuel consumption 考虑航行风险、航行时间和燃料消耗的北极航线多目标优化

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-05 DOI: 10.1016/j.ress.2026.112364

Yuejun Liu , Yang Lu , Yanzhuo Xue , Baoyu Ni , Yutong Jiang , C. Guedes Soares

{"title":"Multi-objective optimisation of arctic shipping routes considering navigational risk, voyage time, and fuel consumption","authors":"Yuejun Liu , Yang Lu , Yanzhuo Xue , Baoyu Ni , Yutong Jiang , C. Guedes Soares","doi":"10.1016/j.ress.2026.112364","DOIUrl":"10.1016/j.ress.2026.112364","url":null,"abstract":"<div><div>This study develops a comprehensive multi-objective dynamic route-planning framework for the Arctic Northeast Passage (NEP) that ensures safe, efficient, and cost-effective navigation of ships by simultaneously optimising voyage time, fuel consumption, and navigational risk, which have become critical concerns. First, an improved A* algorithm is employed to determine the minimum-time, minimum-risk, and minimum-fuel routes under averaged environmental conditions from July to October. Subsequently, a multi-objective weighting and transversal analysis is conducted to identify the Pareto-optimal set of routes, thereby elucidating the trade-offs among conflicting optimisation objectives. To account for the temporal variability of the Arctic environment, daily updated environmental datasets are incorporated, and the LPA* (Lifelong Planning A*) algorithm is applied to construct a dynamic routing model that enables real-time route adaptation to evolving environmental conditions. The proposed framework enables adaptive optimisation based on vessel type, departure date, and operational requirements, thereby facilitating safer, more efficient, and real-time decision-making in Arctic maritime operations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112364"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175266","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

A data-driven risk prediction framework based on multi-criteria sorting considering interaction 考虑交互作用的基于多准则排序的数据驱动风险预测框架

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-04 DOI: 10.1016/j.ress.2026.112354

Mei Cai , Hao Chen , Yaqian Zhang , Shaoyue Sun , Guo Wei

{"title":"A data-driven risk prediction framework based on multi-criteria sorting considering interaction","authors":"Mei Cai , Hao Chen , Yaqian Zhang , Shaoyue Sun , Guo Wei","doi":"10.1016/j.ress.2026.112354","DOIUrl":"10.1016/j.ress.2026.112354","url":null,"abstract":"<div><div>In high-risk domains such as aviation and financial industries, the accuracy and reliability of system safety prediction have become crucial problems. To address the demand for predictability and interpretability of risk prediction, this paper proposes a data-driven risk prediction framework that integrates a novel SHapley Interaction Quantification (SHAP-IQ) algorithm with Multi-Criteria Decision Making (MCDM), termed SI-MCDM. SHAP-IQ, an extended algorithm from SHAP, quantifies multi-order feature interactions to support accurate interpretation of complex model decisions. Choquet Integral, a key aggregation function in MCDM, integrates multiple feature performances and their interaction into an overall evaluation, enabling more realistic risk assessment. The framework innovates through a three-stage methodology: First, data preparation based on Natural Language Processing (NLP) is used to parse unstructured accident reports, converting qualitative narratives into quantifiable risk features; Second, Machine Learning (ML) models coupled with SHAP-IQ identify critical failure modes and elucidate their interactions, thereby revealing nonlinear risk propagation patterns; Finally, Choquet Integral synthesizes interacting factors into an overall risk evaluation, with criteria weights elicited as Shapley importance indices of failure modes—derived from interpreting the XGBoost via SHAP-IQ. The results indicate that the prediction model considering second-order (pairwise) failure mode interactions achieves better performance (Accuracy=91.03%, AUC=0.97), with a 3.27% improvement in accuracy over XGBoost. This framework validates the critical role of multi-factor interactions in risk prediction, and provides dynamic decision-aiding for real-world safety management systems through interpreting situational dependence of interactions. Future research should address bias in accident reports and generalization across domains to enhance robustness and applicability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112354"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175265","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 the robustness of cyber-physical power systems against cross-domain cascading failures: Cyber-physical dynamic reconfiguration 增强网络-物理电力系统对跨域级联故障的鲁棒性：网络-物理动态重构

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-01 DOI: 10.1016/j.ress.2026.112329

Huibin Jia , Jiahe Li , Baichuan He , Shaoyan Li , Zian Cheng , Chunyan Zhao

{"title":"Enhancing the robustness of cyber-physical power systems against cross-domain cascading failures: Cyber-physical dynamic reconfiguration","authors":"Huibin Jia , Jiahe Li , Baichuan He , Shaoyan Li , Zian Cheng , Chunyan Zhao","doi":"10.1016/j.ress.2026.112329","DOIUrl":"10.1016/j.ress.2026.112329","url":null,"abstract":"<div><div>Cross-domain cascading failures represent one of the most significant risks to the safe and stable operation of Cyber-Physical Power Systems (CPPS). Traditional defense mechanisms focus on independent protection within either the physical or cyber domain, making it difficult to effectively address the complex cross-domain propagation process, where failures trigger, propagate, and amplify between the physical and information domains. This paper proposes an cyber-physical joint dynamic reconstruction method based on Software-Defined Networking (SDN) to block the cross-domain propagation of cascading failures, thereby enhancing the robustness of CPPS. First, an SDN-based CPPS architecture is developed, utilizing a Hidden Markov Model (HMM) to represent the dynamic reconstruction process of communication network routing and quantify the impact of network reconfiguration on the observability and controllability of the power grid. Next, a cyber-physical joint reconstruction model with timeliness constraints is established: the cyber layer performs dynamic routing reconstruction to maximize the failure recovery index, while the physical layer triggers optimal power flow to minimize load shedding based on the reconstruction effect. Finally, using node observability, node controllability, and load shedding rates as evaluation metrics, a robustness assessment process based on “dynamic routing-optimal power flow” joint reconstruction for CPPS is proposed. Experimental results from IEEE-30 and IEEE-118 node systems show that during cascading failure propagation, the average load shedding rate under single failure is reduced by 4.59% and 7.46%, respectively, and the average load shedding rate under multiple failures is reduced by 4.22% and 8.32%, significantly improving the robustness of CPPS.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112329"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174841","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

A resilience enhancement approach for interdependent networks incorporating recovery coupling mechanisms 结合恢复耦合机制的相互依赖网络弹性增强方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-07 DOI: 10.1016/j.ress.2026.112375

Jiuyao Jiang, Jichao Li, Tianyang Lei, Kewei Yang

{"title":"A resilience enhancement approach for interdependent networks incorporating recovery coupling mechanisms","authors":"Jiuyao Jiang, Jichao Li, Tianyang Lei, Kewei Yang","doi":"10.1016/j.ress.2026.112375","DOIUrl":"10.1016/j.ress.2026.112375","url":null,"abstract":"<div><div>Modern infrastructure networks are expanding in both scale and interdependence. On the one hand, a localized disturbance can propagate through intra-layer connectivity and inter-layer dependency relations, thereby triggering cascading failures. On the other hand, under interdependence, a disrupted subsystem may draw resources and support from other subsystems to facilitate its own restoration, giving rise to recovery-coupling effects. To bolster resilience to unforeseen threats, we propose an Interdependent Network Resilience Enhancement Framework with Recovery Coupling Mechanisms (INRCM). We formulate a fifth-order tensor that represents a time-varying, two-layer system and jointly embeds recovery-coupling and cascading-failure dynamics. Building on this model, we derive a three-phase structural-resilience metric that evaluates performance in the pre-disturbance, disturbance, and recovery stages. Within INRCM, we develop a graph neural network-guided genetic algorithm as the optimization module to identify high-quality feasible recovery node sets at practical computational cost. Extensive experiments on synthetic and real-world networks across multiple scales, including larger-scale topologies, show that INRCM robustly accelerates post-disturbance recovery and consistently outperforms centrality-based heuristics in both convergence behavior and achieved resilience levels. The framework therefore offers actionable guidance for post-event restoration and for designing infrastructure systems with intrinsic resilience.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112375"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174991","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

Optimizing post-disaster road restoration with reinforcement learning: A traveler-behavior-aware approach 用强化学习优化灾后道路恢复：一种旅行者行为感知方法

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-02-06 DOI: 10.1016/j.ress.2026.112371

Maryam Babaee , Namrata Saha , Frank Mediavilla Ponce , Shabnam Rezapour , M. Hadi Amini

{"title":"Optimizing post-disaster road restoration with reinforcement learning: A traveler-behavior-aware approach","authors":"Maryam Babaee , Namrata Saha , Frank Mediavilla Ponce , Shabnam Rezapour , M. Hadi Amini","doi":"10.1016/j.ress.2026.112371","DOIUrl":"10.1016/j.ress.2026.112371","url":null,"abstract":"<div><div>This paper introduces an AI-driven method to optimize short-term road network restoration in disaster-affected areas, aiming to maximize post-disaster traffic acceleration. Our approach considers travelers' behavior, gradual adaptation to network changes, limited recovery resources, and uncertainties in recovery times. Addressing these complexities requires a stochastic approach for uncertainties, sequential decision-making for resource management, and a model-free technique for simulating traveler adaptation.</div><div>To tackle these challenges, we develop the Traveler-Adaptive Restoration Mechanism (TARM), integrating Reinforcement Learning (RL), the Markov Decision Process (MDP), and optimization-based day-to-day traffic simulation. The method is evaluated on Sioux Falls' road network under tornado scenarios based on historical data. Results highlight the influence of travelers’ route choices and the speed of restoration information dissemination on optimal policies.</div><div>Findings reveal that accelerating the road restoration process by increasing restoration resources does not necessarily enhance the traffic movement efficiency in disaster-affected communities during the disaster response period. Furthermore, we demonstrate that contrary to exiting studies, shortening restoration period is not an appropriate measure of efficiency for post-disaster restoration operations. In fact, reducing the restoration period may adversely impact traffic movements during the response phase in post-disaster situations.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112371"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174992","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

Resilience-driven reinforcement and restoration decision under uncertainty for the interdependent transportation-healthcare system 交通-医疗系统相互依存不确定性下弹性驱动的加固和恢复决策

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-14 DOI: 10.1016/j.ress.2026.112237

Shunshun Pei , Changhai Zhai , Zhuoru Song , Jin Liu , Chenyu Zhang

{"title":"Resilience-driven reinforcement and restoration decision under uncertainty for the interdependent transportation-healthcare system","authors":"Shunshun Pei , Changhai Zhai , Zhuoru Song , Jin Liu , Chenyu Zhang","doi":"10.1016/j.ress.2026.112237","DOIUrl":"10.1016/j.ress.2026.112237","url":null,"abstract":"<div><div>This study develops a resilience enhancement framework for interdependent transportation and healthcare systems (ITHS). A two-stage stochastic optimization model is proposed to support pre-earthquake reinforcement and post-earthquake recovery decisions under budget constraints. The model introduces an enhanced accessibility-based indicator for the ITHS, which incorporates the impact of patient queuing within individual hospital. By integrating the K-means++-based scenario reduction technique, the model aims to minimize the weighted expected functionality loss (<em>FL</em>) and recovery time (<em>WRT</em>) across uncertain scenarios. To improve computational efficiency and solution quality, an ANN-based surrogate model for rapid functionality prediction of ITHS is embedded into the improved adaptive genetic algorithm (AGA), addressing the computational challenges associated with large-scale optimization. The methodological framework is illustrated through an analysis of the ITHS in China, serving as an example of a real-world interdependent system. When applied to an earthquake with a magnitude of 7.8, considering various uncertainties, there are 21 % and 37 % reductions in the <em>FL</em> and <em>WRT</em> of the ITHS after reinforcement.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112237"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174925","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

Resilience assessment and enhancement of urban transportation interdependent network under cascading failure 级联故障下城市交通相互依赖网络的恢复力评价与增强

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-09-01 Epub Date: 2026-01-28 DOI: 10.1016/j.ress.2026.112302

Meng Li, Yu-Rong Song, Bo Song, Guo-Ping Jiang

{"title":"Resilience assessment and enhancement of urban transportation interdependent network under cascading failure","authors":"Meng Li, Yu-Rong Song, Bo Song, Guo-Ping Jiang","doi":"10.1016/j.ress.2026.112302","DOIUrl":"10.1016/j.ress.2026.112302","url":null,"abstract":"<div><div>Urban transportation systems are essential for sustaining urban growth and ensuring efficient resource allocation. Existing studies primarily focus on evaluating network resilience after system disturbances, with insufficient attention paid to the response mechanisms during disturbances and the enhancement of resilience afterward. Therefore, we propose a cascading failure model that considers passenger transfer impedance, and design a recovery priority strategy for failed nodes to maximize the resilience of the urban transportation interdependent network (UTIN). Specifically, based on traffic sensing data, we construct a station-centric UTIN to assess structural resilience under various disruption scenarios and different transfer distances. By combining impedance function and flow redistribution, passenger behavior and node load update are considered. Additionally, the recovery priority strategy for failed nodes is discussed. The results indicate: 1) UTINs with longer transfer distances exhibit stronger resistance to risks. When considering impedance costs, the optimal transfer distance is 800 m. 2) During cascading failure propagation, optimizing flow distribution effectively lowers the critical capacity threshold required for system stability, thereby enhancing network resilience. 3) During the recovery phase, different recovery strategies exhibit significant differences in their effectiveness in restoring system resilience. The research findings provide valuable references for disaster prevention, emergency response, and post-disaster recovery in urban transportation systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112302"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098523","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

Reliability estimation for the multicomponent stress-strength model based on objective Bayesian method 基于客观贝叶斯方法的多分量应力强度模型可靠性估计

IF 11 1区工程技术

Reliability Engineering & System Safety Pub Date : 2026-07-01 Epub Date: 2026-01-11 DOI: 10.1016/j.ress.2026.112209

Tiefeng Zhu

{"title":"Reliability estimation for the multicomponent stress-strength model based on objective Bayesian method","authors":"Tiefeng Zhu","doi":"10.1016/j.ress.2026.112209","DOIUrl":"10.1016/j.ress.2026.112209","url":null,"abstract":"<div><div>The multicomponent stress-strength model has many important applications in reliability analysis. Most existing studies assume that stress and strength follow the same distribution and employ maximum likelihood (ML) estimation for reliability inference. However, this assumption restricts the model’s applicability, as there is often no physical rationale for requiring identical distributions for stress and strength. Hence, this paper discusses the reliability inference of a multicomponent stress-strength model under the assumption that the strength and stress variables belong to different distributions. An objective Bayesian method (OBM) framework is applied to infer the model parameters and system reliability based on derived Jeffreys and two reference priors. To ensure the validity of reliability inference, the proper properties of posterior distributions of the model parameters are proved and a Gibbs sampling algorithm is developed. Simulations are implemented to compare the OBM with the considered methods and the results show the superiority of the proposed OBM for the small sample size case. Finally, one real example is analyzed for illustrative purposes.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112209"},"PeriodicalIF":11.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145963112","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