Reliability Engineering & System Safety最新文献

{"title":"A multi-model fusion redundancy allocation method for subsea control systems with consideration of Bayesian stress-conditional importance","authors":"Yinhang Zhang ,&nbsp;Baoping Cai ,&nbsp;Chuntan Gao","doi":"10.1016/j.ress.2026.112266","DOIUrl":"10.1016/j.ress.2026.112266","url":null,"abstract":"<div><div>Subsea control systems operating under high hydrostatic pressure and low temperatures require high reliability. Redundancy is a well-established method for improving reliability and availability and for reducing the frequency of replacements. Nevertheless, under extreme subsea conditions with multi-source uncertainties, redundancy design may induce a redundancy paradox: nonlinear growth in resource consumption can ultimately reduce overall system reliability. Therefore, an optimization framework that unifies stress condition characterization, component-importance quantification, and reliability–cost coupling is essential. To address this challenge, a multi-model fusion-based redundancy allocation method for subsea control systems under Bayesian stress-conditional importance is proposed. This methodology establishes an end-to-end redundancy allocation framework, integrating multiple models while unifying the modelling process and coupling mechanisms. A Bayesian stress-conditional importance metric is introduced as the basis for redundancy decisions, and uncertainties in the impact of component failures on system reliability are addressed. Systematically evaluates component importance, feasibility, and redundancy allocation, transforming the redundancy assignment problem into a multi-objective optimization problem. Subsequently, a multi-objective particle swarm optimization algorithm incorporating the Bayesian stress-conditional importance metric is employed to determine the system's optimal redundancy configuration, aiming to maximize system reliability while minimizing costs. The method is validated against benchmark results from a typical series–parallel system and a complex bridge system. A case study on a subsea light intervention equipment control system further demonstrates high reliability and accurate optimization performance. Overall, the proposed approach supports high-reliability design and contributes to safe and dependable operation of subsea control systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112266"},"PeriodicalIF":11.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079478","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":"Coordinative optimization strategy for group track maintenance planning and train scheduling of railways","authors":"Shanshan Yan,&nbsp;Dongming Fan,&nbsp;Bo Li,&nbsp;Ziguang Ji,&nbsp;Yue Zhang,&nbsp;Yi Ren","doi":"10.1016/j.ress.2026.112272","DOIUrl":"10.1016/j.ress.2026.112272","url":null,"abstract":"<div><div>Tracks are essential for ensuring the safety and reliability of railway transportation services. Due to the high-density operation of trains and exposure to open-air environmental conditions, components within the track system are susceptible to failure resulting from aging. The issue is typically addressed through concentrated preventive maintenance. However, the maintenance planning overlooks the influence of structural dependencies among track components and must be coordinated with the train timetable to be completed on time without affecting train operations. Therefore, this study proposes a coordinative optimization strategy for group track preventive maintenance planning and train scheduling considering structural dependencies. Track maintenance projects and activities are analyzed to illustrate the grouping conditions that consider structural dependencies, and a mixed-integer linear programming model is established to formulate this problem. Numerical experiments on multiple scales are presented to illustrate the sensitivity of the proposed approach. The impacts of maintenance capacity, group requirements, and the interval between trains traveling in the same direction on the model are also discussed. For the Guiyang-Kunming railway line, an actual case study demonstrated that the collaborative strategy enables a total cost reduction of 31.68%. Additionally, several recommendations are proposed for managers.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112272"},"PeriodicalIF":11.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079553","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":"Uncertainty-aware sensorless anomaly detection using a reliable indicator from position-guided multi-step deep decomposition network","authors":"Junyu Qi ,&nbsp;Hamid Reza Karimi ,&nbsp;Yannick Uhlmann ,&nbsp;Zhuyun Chen ,&nbsp;Weihua Li ,&nbsp;Gernot Schullerus","doi":"10.1016/j.ress.2026.112258","DOIUrl":"10.1016/j.ress.2026.112258","url":null,"abstract":"<div><div>Condition monitoring (CM) and predictive maintenance (PdM) are essential for ensuring reliability and efficiency in intelligent manufacturing. While bearings and gears have been extensively studied, roller chains have received limited attention, primarily due to the difficulty of installing contact accelerometers on moving chains and the high cost of deploying sensors across long spans. Consequently, effective CM techniques for roller chains remain an open challenge. This study introduces a sensorless CM framework that relies solely on motor driver signals, eliminating the need for additional physical sensors. Motor torque and position data are acquired and processed using a Position-guided Multi-Step Deep Decomposition Network (PMSDDN), a lightweight neural architecture designed to construct a reliable health indicator (HI). PMSDDN segments the torque signal based on the motor position, decomposes each segment into low- and high-frequency components, and predicts them independently through efficient linear modules. This decomposition reduces noise and fluctuations, producing a smooth degradation trend that enhances interpretability. Compared with traditional HIs and state-of-the-art deep learning models, PMSDDN delivers higher effectiveness and computational efficiency. For anomaly detection, a Weighted K-Nearest Neighbors (WKNN) method is developed, combining three different statistical measures with uncertainty quantification to improve robustness and sensitivity to early degradation. The framework is validated on nine roller chains under three operating conditions. Results confirm superior performance in both health assessment and anomaly detection, highlighting its potential as a practical and scalable CM solution for roller chain systems in modern manufacturing environments.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112258"},"PeriodicalIF":11.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079657","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":"An integrated framework for functional model-based safety assessment of process systems using Cloud-Bayesian network","authors":"Ruixue Li,&nbsp;Jing Wu,&nbsp;Ole Ravn,&nbsp;Xinxin Zhang","doi":"10.1016/j.ress.2026.112231","DOIUrl":"10.1016/j.ress.2026.112231","url":null,"abstract":"<div><div>The increasing complexity of modern industrial systems makes it challenging to fully consider the complex interactions between components while also representing dynamic system behaviors. This poses challenges for traditional risk assessment methods, which are often labor-intensive and time-consuming. Model-Based Safety Assessment (MBSA) emerges as a promising solution, offering concrete knowledge representation and consistent reasoning. MBSA not only processes large system data volumes and manages complexity through structured modeling but also automates the error-prone manual safety analysis process, enhancing efficiency and reliability.</div><div>Multilevel Flow Modelling (MFM), a model-based method belonging to symbolic AI with cognitive capabilities, provides a functional modeling framework to represent complex industrial processes. It captures mass, energy, and control information, enabling effective reasoning about failure propagation and system behavior. To quantify the qualitative reasoning conducted by MFM-based hazard analysis, Bayesian Network (BN) is introduced to enable a more comprehensive utilization of MFM for safety assessments. In cases of insufficient failure scenario data, subjective information with uncertainties from experts remains valuable. The improved Cloud model is proposed to process expert judgments, addressing cognitive fuzziness and stochasticity issues.</div><div>This paper proposes a model-based safety assessment framework that enhances the MFM-based hazard analysis by probabilistic risk reasoning using Cloud-Bayesian Network. This framework facilitates critical hazard and failure propagation analysis while also enabling scenario verification through downstream effect analysis caused by varying degrees of deviation. The framework is demonstrated in the Minox system of an offshore oil &amp; gas platform, providing critical insights for designing effective countermeasures and aiding decision-makers in enhancing risk management.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112231"},"PeriodicalIF":11.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079548","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":"Detection framework for bidirectional false data injection attacks to enhance reliability in cyber-physical power systems","authors":"Bo Zhang ,&nbsp;Min Du ,&nbsp;Haofeng Zheng ,&nbsp;Kaiyang Liu ,&nbsp;Baochun Lu","doi":"10.1016/j.ress.2026.112267","DOIUrl":"10.1016/j.ress.2026.112267","url":null,"abstract":"<div><div>With the rapid development of cyber attack techniques, cyber-physical power systems (CPPSs) are facing the threat of more customized and stealthier cyber attacks. Among them, bidirectional false data injection attacks (BFDIAs) have become one of the most destructive types of attacks. However, existing research on false data injection attacks (FDIAs) mostly focuses on the detection of unidirectional FDIAs, and the existing methods have limited effectiveness in detecting highly stealthy BFDIAs. In light of this, the paper proposes a detection method for BFDIAs in CPPSs based on a multi-level detection model. Firstly, an optimization-based convolutional neural network is employed to perform shallow application-layer BFDIAs detection and filter out abnormal messages in the IEC60870–104 protocol (IEC 104) data. Subsequently, the filtered IEC104 dataset is fed into a covariance matrix model to detect conventional BFDIAs in the deep application layer, and the detection results are integrated into the original alarm dataset in the form of alerts. Furthermore, the set of alarm events is input into a bidirectional hidden Markov model to detect highly stealthy BFDIAs in the deep application layer. The proposed multi-level detection framework leverages a three-stage mechanism—MFO<img>CNN–based shallow application-layer detection, covariance-based deep-layer detection, and bidirectional hidden Markov modeling—to integrate synergistically optimized learning with temporal correlation analysis and thereby enhance the detection performance against highly stealthy bidirectional FDIAs. Finally, the proposed method is validated for BFDIAs detection in a real-world 126-nodes power system, demonstrating its accuracy and practicality.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112267"},"PeriodicalIF":11.0,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079468","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":"Causal analysis of ship inspection data and maritime accidents through causal neural networks","authors":"Run Liu ,&nbsp;Mingyang Zhang ,&nbsp;Ran Yan ,&nbsp;Zaili Yang","doi":"10.1016/j.ress.2026.112255","DOIUrl":"10.1016/j.ress.2026.112255","url":null,"abstract":"<div><div>Maritime transportation serves as the backbone of international trade, yet its growth is accompanied by an increasing number of maritime accidents. Therefore, reducing maritime accidents and mitigating their impact are crucial to supporting sustainable maritime industry development. Port state control (PSC) inspection is a key regulatory tool for enhancing maritime safety by inspecting foreign visiting ships to port. However, the actual impact of PSC inspection on reducing maritime accidents and how it can be further optimised remains unclear. This study employs a causal generative neural network model to explore the causal relationship among ship particulars, historical PSC inspection results, and future maritime accidents through a directed acyclic graph (DAG). By integrating causal discovery and causal inference, this study provides a robust framework for modelling maritime accident causation and overcomes limitations of traditional methods that often rely on discretisation or linear assumptions. Based on the identified optimal causal DAG, this study conducts decile-level causal intervention analyses on key contributing variables to quantify their effects on both the occurrence and severity of various types of maritime accidents. The results indicate that the optimal maximum PSC inspection interval is 409–509 days, which is consistent with the existing longest inspection time window (10–18 months) in the Tokyo Memorandum of Understanding. The probability and severity of maritime accidents exhibit a U-shaped relationship with the mean inspection interval, and the lowest risk occurs at 170–210 days. Additionally, the life-saving equipment conditions should be paid more attention in the PSC inspection. Due to the quantitative analysis nature, the new method will be able to help analyse the quantity of the increased or decreased risks against different types of maritime accidents, supporting a comprehensive perspective for safety management and accident prevention strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112255"},"PeriodicalIF":11.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079549","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":"From physics to machine learning and back: Part I - Learning with inductive biases in prognostics and health management (PHM)","authors":"Olga Fink,&nbsp;Vinay Sharma ,&nbsp;Ismail Nejjar ,&nbsp;Leandro Von Krannichfeldt,&nbsp;Sergei Garmaev,&nbsp;Zepeng Zhang,&nbsp;Amaury Wei,&nbsp;Gaetan Frusque,&nbsp;Florent Forest,&nbsp;Mengjie Zhao,&nbsp;Chi-Ching Hsu,&nbsp;Keivan Faghih Niresi,&nbsp;Han Sun,&nbsp;Hao Dong,&nbsp;Chenghao Xu,&nbsp;Raffael Theiler,&nbsp;Arthur Bizzi,&nbsp;Kevin Steiner","doi":"10.1016/j.ress.2026.112213","DOIUrl":"10.1016/j.ress.2026.112213","url":null,"abstract":"<div><div>Prognostics and Health Management (PHM) is essential for ensuring the safe, reliable, and efficient operation of complex engineered systems by integrating fault detection, diagnostics, and prognostics into a unified framework. While machine learning (ML) has significantly advanced PHM by enabling data-driven decision-making, real-world challenges such as sparse or noisy data, limited labels, and complex degradation dynamics require approaches that go beyond purely data-driven modeling. This review focuses on <strong>inductive bias</strong> – the integration of prior knowledge, physical laws, and structural assumptions into ML model design – as a foundational mechanism to improve generalization, robustness, and interpretability in PHM. We explore the current state of the art in applying inductive bias to PHM, reviewing a wide range of methods including graph neural networks (relational biases), state-space models and neural ordinary differential equations (temporal biases), signal processing-inspired learning (spectral biases), neural operators (operator biases), causal representation learning (causal biases), and interpretable-by-design models (interpretability biases) through the use of inductive bias. For each method, we discuss its advantages, limitations, and suitability for different PHM tasks, and identify emerging applications where these techniques show strong potential. Furthermore, we examine how ML contributes back to physics understanding through symbolic regression (rediscovering physical laws) and post-hoc interpretation (transparent decision-making), closing the loop between physics understanding and modeling in PHM. By embedding domain knowledge into learning architectures, these approaches help constrain the hypothesis space and promote physically consistent learning, bridging the gap between theoretical modeling and real-world deployment in safety-critical applications. Part II of this review explores observational and learning biases, focusing on how data augmentation, representation, and training strategies shape model behavior and further enhance alignment between machine learning and the physical systems it aims to monitor and manage.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112213"},"PeriodicalIF":11.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039534","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":"Impact of heterogeneous behaviors of subarea managers on the recovery of urban water distribution systems after disaster","authors":"Shu Chen ,&nbsp;Yuxin Liu ,&nbsp;Zhijie Liu ,&nbsp;Yuwei Liu ,&nbsp;Nan Li ,&nbsp;Fei Wang","doi":"10.1016/j.ress.2026.112262","DOIUrl":"10.1016/j.ress.2026.112262","url":null,"abstract":"<div><div>Urban Water Distribution Systems (WDSs) are critical for societal functioning. Disasters often cause widespread damage, segmenting the network into isolated subsystems. Due to communication breakdowns, subarea managers must make recovery decisions based on limited information and their own experience, leading to heterogeneity in management behaviors. Although existing studies typically assume a unified decision-making model, they have overlooked the decision-making characteristics of subarea management and its impact on the post-disaster recovery process of the WDSs. To address this gap, this study proposes an integrated framework that combines hydraulic simulation with subarea decision-making behavior to uncover the impact of heterogeneous behaviors of subarea managers on the recovery of urban WDSs after disaster. Firstly, a seismic scenario is simulated to determine the damage state of nodes and pipelines within the system, defining the initial recovery scenario post-disaster. Secondly, based on different decision preferences and objectives, five typical recovery strategies are proposed. Furthermore, this study develops a model for hydraulic simulation and decision-making processes. Finally, this study quantifies managerial behavioral heterogeneity using restoration time and system functionality. To validate the effectiveness of the proposed framework, it is first applied to a baseline-scale network. The results demonstrate that different strategies lead to significant variations in recovery efficiency, while the number of partitions nonlinearly influences restoration outcomes, with the 2-subarea configuration consistently achieving optimal performance across all tested scenarios. This study is further validated through multiple extended cases of varying scales, with consistent results across different system sizes. These findings reveal the significant impact of subarea managers' heterogeneous behaviors on the recovery process of WDSs after disaster. The conclusions provide theoretical support for post-disaster recovery strategies, offering guidance for future subarea management optimization and practical emergency decision-making.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112262"},"PeriodicalIF":11.0,"publicationDate":"2026-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079481","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":"Two-stage rolling optimization resilience enhancement strategy for Multi-Microgrid under extreme weather conditions","authors":"Changyun Li,&nbsp;Rongyu Zhang,&nbsp;Hangyu Lin","doi":"10.1016/j.ress.2026.112259","DOIUrl":"10.1016/j.ress.2026.112259","url":null,"abstract":"<div><div>In response to the significant reduction in wind power and photovoltaic output, along with large-scale power outages caused by extreme weather conditions such as strong winds, rainstorms, and blizzards, a two-step rolling resilience improvement strategy for multi-microgrid (MMG) systems is proposed. First, the paper analyzes existing microgrid (MG) resilience metrics and introduces an MMG resilience metric that considers load shedding and an emergent resilience improvement rate, characterizing the resilience enhancement of each sub-microgrid (SMG). A distributed energy management model for SMGs and an energy management model for MMG under normal weather conditions are then established. Through internal coordination within the MMG, power exchange is implemented to minimize system operating costs. Secondly, a two-stage rolling resilience enhancement strategy for MMG is proposed, ensuring optimal system operating costs with minimal load shedding. Finally, to address the inequitable distribution of benefits among SMGs participating in the joint operation of MMG, the Banzhaf value method is used for fair distribution of synergistic economic benefits. The effectiveness of the proposed strategy in enhancing the resilience of both the overall MMG system and each SMG is verified through a case study.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112259"},"PeriodicalIF":11.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146039532","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":"Policy modeling for urban energy resilience to extreme heat: A multi-agent simulation framework in Chongqing, China","authors":"Yueting Ding ,&nbsp;Feng Chen ,&nbsp;Mariia Plotnikova ,&nbsp;Bo Wang","doi":"10.1016/j.ress.2026.112260","DOIUrl":"10.1016/j.ress.2026.112260","url":null,"abstract":"<div><div>Climate change is increasing extreme heat events, intensifying stress on urban energy systems. Chongqing, a megacity in China, has seen a notable rise in urban heat island intensity (∼0.5 °C per decade), driving peak cooling loads up by 15 %. While previous research emphasized energy efficiency, resilience under compounded extreme heat remains underexplored. To fill this gap, we develop an agent-based model with four agent types exhibiting adaptive thermal behavior, simulating building energy demand under four high-temperature scenarios (36∼42 °C) with non-linear responses. A multi-indicator framework is employed to evaluate three strategies: adjusting temperature thresholds, establishing public cooling centers (PCCs), and electricity rationing. Results indicate: 1) Proportional electricity rationing is more cost-effective and better mitigates oversupply risks than uniform restrictions. 2) Increasing temperature thresholds from 26 °C to 28 °C improves resilience (relative closeness: 56 %) with only marginal cost increases, although economic burdens remain notable. Expanding PCCs provides negligible resilience benefits (&lt;0.1 % improvement). 3) A TOPSIS-based cost-resilience trade-off analysis further confirms that temperature threshold adjustment consistently offers superior performance in balancing resilience outcomes and economic feasibility. Our findings highlight the importance of incorporating economic and practical feasibility into urban energy resilience strategies.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"271 ","pages":"Article 112260"},"PeriodicalIF":11.0,"publicationDate":"2026-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079937","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}