Reliability Engineering & System Safety最新文献

{"title":"A prior knowledge-guided predictive framework for LCF life and its implementation in shaft-like components under multiaxial loading","authors":"Butong Li,&nbsp;Junjie Zhu,&nbsp;Xufeng Zhao","doi":"10.1016/j.ress.2025.111044","DOIUrl":"10.1016/j.ress.2025.111044","url":null,"abstract":"<div><div>Predicting low-cycle fatigue (LCF) life under complex loading conditions has long been a challenge. Reliable fatigue life prediction is crucial for the fatigue reliability assessment of industrial components. This paper proposes a prior knowledge-guided framework for predicting LCF life under multiaxial loading conditions. The framework integrates physical knowledge and partial known relationships. Inspired by concepts from reliability-based design optimization (RBDO), the framework is capable of predicting deterministic and probabilistic LCF life with greater efficiency and accuracy. Building on this, we discuss the LCF life degradation of components under survival rates by introducing the multiaxial fatigue parameter. The parameter can effectively describe the trends of LCF life under elliptical multiaxial loading paths. Furthermore, the hazard rate and fatigue reliability of structural components is investigated. The research presented in this paper can offer valuable guidance for applications in practical industrial contexts.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111044"},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724797","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":"Preventive replacement policies of parallel/series systems with dependent components under deviation costs","authors":"Jiale Niu ,&nbsp;Rongfang Yan ,&nbsp;Jiandong Zhang","doi":"10.1016/j.ress.2025.111033","DOIUrl":"10.1016/j.ress.2025.111033","url":null,"abstract":"<div><div>In traditional reliability analysis, an important assumption is that the failure components are independent from one another. However, this assumption is often challenged in reliability engineering as practical failure process is always dependent on one another component. This manuscript bases on the method of copula to characterize the dependence of the components lifetime, and studies the preventive replacement policies of series/parallel systems with dependent heterogeneous components under deviation costs. Firstly, we provide the optimal replacement time of the series and parallel systems from dependent components in the age replacement policies under the deviation cost. Secondly, we give the optimal number of periods for series/parallel systems with dependent components to minimize the expected cost rate under deviation costs. The effects of dependency, deviation costs, and the number of components in the system on the maintenance policies are analyzed. The numerical examples are then implemented to develop the optimal preventive replacement policies that minimize the expected cost rate. A case study of the cables in high-voltage transmission networks system from dependent components is conducted to present the optimal replacement time and number of periods.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111033"},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714826","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":"Cost-Informed Risk-based Inspection (CIRBI) for Hydrogen Systems Components: A Novel Approach to Prevention Strategies","authors":"Leonardo Giannini ,&nbsp;Genserik Reniers ,&nbsp;Ming Yang ,&nbsp;Maria Nogal ,&nbsp;Nicola Paltrinieri","doi":"10.1016/j.ress.2025.111063","DOIUrl":"10.1016/j.ress.2025.111063","url":null,"abstract":"<div><div>The evolving energy landscape in Europe is showing concrete signals that hydrogen will play a central role in the energy transition scenario. In this light, a report of the European Hydrogen Backbone pinpoints no less than forty existing projects focused on the commissioning of several kilometers of hydrogen pipelines in the following years. Hence, ensuring a safe operability of these systems represents a topic worthy of investigation and marked by significant challenges, especially given the unique properties that make hydrogen a potentially hazardous substance. Established techniques may prove helpful in supporting the development of dedicated prevention and mitigation strategies for hydrogen systems. Among these, Risk-Based Inspection (RBI) could represent an effective tool to design inspection programs aimed at the detection of hydrogen-induced damages, especially for components working in pressurized environments, including pipeline materials. However, the lack of operational experience associated with emerging technologies may lead to the adoption of over-conservative safety measures, which could impact the economic attractiveness of these systems. Therefore, this study proposes an evolution of conventional RBI planning by implementing concepts of safety economics and optimization modelling, thus building a novel approach named “Cost-Informed Risk-Based Inspection” (CIRBI). The proposed methodology is therefore applied to a case study of inspection techniques potentially suitable for pipeline materials (i.e., API X-series pipeline steels), showcasing its potential as a self-standing approach for inspection planning while also demonstrating the insight that it may provide to ensure a safe operability of hydrogen pipelines.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111063"},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714828","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":"Improving the operational reliability of aeroengines based on coupled thermodynamics and dynamics stochastic responses","authors":"Dawen Huang,&nbsp;Dengji Zhou","doi":"10.1016/j.ress.2025.111051","DOIUrl":"10.1016/j.ress.2025.111051","url":null,"abstract":"<div><div>The gas path system is a special mechanical system that is the core of thrust generation in aeroengines. Random disturbances, both internal and external, including intake and combustion heat random excitations, can cause the gas path system to vary from its equilibrium state and diverge over time, which will reduce the system stability, and even cross the stability boundary to increase the safe operation risk. The impact of random excitations is not fully taken into account by the usual approach of guaranteeing safe and reliable operation with a larger safety factor, which leads to a limited operating range and makes it difficult to fully utilize the gas path performance. This work establishes a correlation expression between the aerothermodynamics and rotor dynamics characteristics of gas path systems using random torques as transfer parameters, and establishes the dynamics model and energy function through random torques and rotor state parameters. Based on the stochastic responses of generalized momentum and energy function, the stochastic dynamics characteristics and stability of rotors and gas path systems were analyzed, and a random excitation critical strength spectrum is formed to ensure the reliable and safe operation of the engine under multi-source random excitation. The results indicate that random excitations make the system response more unpredictable, which lead to the system transition from a stable to an unstable state, resulting in lower reliability. The effects of random excitations over the whole-time scale can be seen in the energy function. Moreover, the operational reliability is improved while enhancing the gas path performance through the application of stochastic response. The effectiveness of the method and the correctness of the results were verified through compressor working lines under different random excitations. This work studies the stochastic responses and reliability of gas path systems from the perspective of coupling thermodynamics and dynamics, and realizes the application of stochastic stability in improving operational reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111051"},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704005","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 cross-layer security risk assessment and mitigation for cyber-physical power systems","authors":"Pengchao Yao ,&nbsp;Qiang Yang ,&nbsp;Wenhai Wang","doi":"10.1016/j.ress.2025.111027","DOIUrl":"10.1016/j.ress.2025.111027","url":null,"abstract":"<div><div>Cyber-attacks targeting cyber-physical power systems (CPPSs) are increasingly recognized as complex and persistent cyber-to-physical (C2P) security threats, which introduce substantial cross-layer risks to critical power infrastructures. However, existing security frameworks fail to provide a comprehensive approach for risk assessment and mitigation against these ongoing and stealthy cross-layer attacks in CPPSs. This paper presents a cross-layer security risk management method that enables dynamic evaluation of cyber-physical security risks and the formulation of optimal defense strategies to reduce those risks. Specifically, an Extended Hierarchical Bayesian Attack Graph (EHBAG) is introduced to model the C2P attack risk propagation, which can infer the probability of physical-space incidents occurring based on detected attack nodes in the cyber layer. Observation nodes are incorporated into the EHBAG to represent uncertainty in the detected evidence. An attack surface generation algorithm is used to identify the most dangerous set of detected attack nodes within the EHBAG that require immediate attention. Then, a multi-objective security decision-making approach is presented to derive the optimal strategy for defending the highest-value nodes within the attack surface, aiming to reduce the cyber-physical security risks of the system. The proposed approach is implemented and evaluated using a real-world CPPS testbed and the numerical results confirmed its feasibility and effectiveness for risk assessment and mitigation.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111027"},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747724","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 uncertainty reduction-guided single-loop Kriging coupled with subset simulation for time-dependent reliability analysis","authors":"Fukang Xin ,&nbsp;Pan Wang ,&nbsp;Yi Chen ,&nbsp;Rong Yang ,&nbsp;Fangqi Hong","doi":"10.1016/j.ress.2025.111065","DOIUrl":"10.1016/j.ress.2025.111065","url":null,"abstract":"<div><div>Time-dependent reliability analysis (TRA) of rare failure events is an imperative task in structural engineering, and the single-loop Kriging method is considered one of the most promising methods when dealing with time-consuming performance functions. However, the adaptive learning strategy still has improvement potential to achieve a better balance of accuracy and efficiency. To fill this gap, a new uncertainty reduction-guided single-loop Kriging coupled with subset simulation (UR-SLK-SS) method is proposed for TRA. Firstly, the uncertainty estimation of each intermediate failure probability is derived to measure the contribution of each time trajectory, which includes not only the individual contribution of the time trajectories but also the correlated contribution between the time trajectories. Then, a learning function for simultaneously selecting new random samples and time nodes is built. In the time trajectories with the largest uncertainty, the time node with the largest reduction in uncertainty is selected for adaptive updating. Further, a two-stage stopping criterion is proposed to guarantee the accuracy of the results as well as higher efficiency. Seven case studies are presented to demonstrate the better capability of the proposed method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111065"},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143739301","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":"Addressing systemic risks in autonomous maritime navigation: A structured STPA and ODD-based methodology","authors":"Takuya Nakashima ,&nbsp;Rui Kureta ,&nbsp;Siddartha Khastgir","doi":"10.1016/j.ress.2025.111041","DOIUrl":"10.1016/j.ress.2025.111041","url":null,"abstract":"<div><div>This paper presents a novel approach to managing unexpected risks for autonomous ships by addressing the unique challenges posed by their operational complexity and human–machine interactions. Autonomous ships operate in diverse environments, and their tasks span long-term voyage planning, short-term collision avoidance, and ship motion control, making traditional risk assessment methods insufficient. This paper proposes an enhanced methodology based on System-Theoretic Process Analysis (STPA), focusing on discrepancies in process models of several controllers to identify systemic risks in a structured manner. Additionally, the study introduces the importance of the Operational Design Domain (ODD) concept for autonomous ships, reflecting the specific conditions of maritime operations. It also proposes how to evaluate ODD and relevant loss scenarios extracted from STPA by applying several metrics for its safe control. Through a case study using Japan’s autonomous ship demonstration project, the methodology’s effectiveness is demonstrated by applying it to an autonomous container ship. Finally, the paper argues that by systematically refining ODD taxonomies and ensuring a holistic understanding of process models, autonomous ships can achieve safety assurance comparable to that of conventional vessels, despite the inherent challenges of maritime navigation.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111041"},"PeriodicalIF":9.4,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747719","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":"Enhancing aircraft reliability with information redundancy: A sensor-modal fusion approach leveraging deep learning","authors":"Jie Zhong,&nbsp;Heng Zhang,&nbsp;Qiang Miao","doi":"10.1016/j.ress.2025.111068","DOIUrl":"10.1016/j.ress.2025.111068","url":null,"abstract":"<div><div>Redundancy design is a critical way to enhance the reliability and safety of aircraft. However, hardware redundancy significantly increases manufacturing costs and system complexity, while analytical redundancy faces challenges in establishing accurate mathematical models. To address these issues, this paper proposes an information redundancy method for flight data based on sensor-modal fusion. This method leverages deep learning networks to learn the complex coupling relationships between flight parameters from a vast amount of historical flight data. In this respect, a mapping model for flight parameters is established to replace traditional mathematical models used for analytical redundancy. First, the traditional sliding window process is improved by proposing a Fibonacci sampling to balance computational resources and historical view length. Next, a sensor-modal fusion-based prediction model is designed to avoid spatial interactions among sensor features during feature extraction. Furthermore, a sensor attention module and a modal attention module is employed to improve the interpretability of the model. Finally, a Lebesgue evaluation metric is introduced to address ineffective assessment under state balance conditions. The proposed method was validated using real flight data. The results demonstrate that the Lebesgue mean absolute error remained below 1.4 %, outperforming all comparative methods and affirming the effectiveness and superiority of the proposed method. Furthermore, this paper investigated the potential of information redundancy in enhancing aircraft reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111068"},"PeriodicalIF":9.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747718","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":"Balancing information and predictability: A pan latent feature model for plant-wide oscillations root cause analysis","authors":"Yang Wang,&nbsp;Yining Dong","doi":"10.1016/j.ress.2025.111036","DOIUrl":"10.1016/j.ress.2025.111036","url":null,"abstract":"<div><div>Analyzing the root cause for plant-wide oscillations is critical for maintaining the reliability and safety of complex systems with control loops. Oscillations in complex systems display varying degrees of predictability and information content. However, existing methods typically focus on a single aspect, which inherently restricts their comprehensiveness, flexibility, and accuracy of diagnosis. To address these challenges, this paper presents a novel pan-latent feature (PLF) modeling-based root cause analysis approach for plant-wide oscillations. PLF flexibly explores both predictability and information content within a unified model to extract informative, predictable, and a novel type of intermediate LFs that balance both attributes, enabling the comprehensive and flexible extraction of multi-type oscillations. By establishing explicit relationships between the extracted features and the original variables, PLF diagnoses the root cause variables of the extracted multi-type oscillations, providing multi-perspective diagnosis results. Through a numerical case study and a real-world plant-wide oscillation application, the proposed method demonstrates superior comprehensiveness, flexibility, and accuracy in finding the root variables of multi-type oscillations compared to existing approaches.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 111036"},"PeriodicalIF":9.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734649","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":"Markov chain-based model for IoT-driven maintenance planning with human error and spare part considerations","authors":"Vahideh Bafandegan Emroozi ,&nbsp;Mahdi Doostparast","doi":"10.1016/j.ress.2025.111052","DOIUrl":"10.1016/j.ress.2025.111052","url":null,"abstract":"<div><div>This study introduces a Markov-based optimization framework for maintenance and spare parts inventory management, enhancing cost efficiency and operational reliability in cement production. By leveraging steady-state probabilities, the model integrates real-time equipment monitoring via the Industrial Internet of Things (IoT), reducing manual inspections and mitigating human errors. A comprehensive analysis demonstrates that level-2 preventive maintenance (PM) achieves the highest steady-state probability, effectively balancing cost minimization and system reliability over a 36-period planning horizon. Key optimization variables include the IoT adoption rate (<strong>γ = 0.72</strong>), human error probability (HEP) (<span><math><msup><mrow><mi>p</mi></mrow><mrow><mo>[</mo><mrow><mi>T</mi><mi>o</mi><mi>t</mi><mi>a</mi><mi>l</mi></mrow><mo>]</mo></mrow></msup></math></span> = 0.137), and total cost objective (<span><math><msup><mrow><mi>z</mi></mrow><mrow><mo>[</mo><mrow><mi>T</mi><mi>o</mi><mi>t</mi><mi>a</mi><mi>l</mi></mrow><mo>]</mo></mrow></msup></math></span>= 3151,385 currency <strong>units</strong>). The model dynamically adjusts inventory replenishment policies to minimize stockouts and reliance on costly emergency orders. Results indicate that the proposed framework significantly improves maintenance scheduling, optimizes resource allocation, and reduces operational downtime. Furthermore, the study underscores the model's adaptability and its potential for integration with predictive analytics, paving the way for intelligent, data-driven maintenance strategies. These findings provide a strong foundation for advancing industrial maintenance optimization and operational efficiency.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"261 ","pages":"Article 111052"},"PeriodicalIF":9.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747720","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}