Reliability Engineering & System Safety最新文献

{"title":"Quantitative assessment of language and cultural diversity as human reliability factors in maritime safety","authors":"Rafi Ullah Khan ,&nbsp;Jingbo Yin ,&nbsp;Muhammad Afzaal ,&nbsp;Zaili Yang","doi":"10.1016/j.ress.2026.112358","DOIUrl":"10.1016/j.ress.2026.112358","url":null,"abstract":"<div><div>Reliable shipboard operations depend on effective communication and situational awareness among multinational crews. Language barriers and cultural diversity, if unmanaged, can degrade operational reliability and compromise safety-critical decision-making. Yet quantitative evidence on how language and cultural diversity shape accident risk remains limited. This study applies a Bayesian Network (BN) framework to assess the reliability and safety implications of language and cultural diversity using 550 maritime accident investigations. Natural Language Processing (NLP) extracted indicators, including crew nationality composition, language diversity (Shannon Index), cultural diversity (Hofstede Index), and adherence to Standard Maritime Communication Phrases (SMCP), which were integrated into the BN model. Parameters were estimated using the Expectation Maximization (EM) algorithm, allowing the model to quantify pathways to communication errors, conflict, and accident severity. Results show that high language diversity and low proficiency substantially increase communication failures: miscommunication and misinterpretation together account for 68% of observed errors (43% and 25%), with high language diversity the most frequent state (∼48%). Verbal exchanges remain the most failure-prone mode (47%), and container ships account for the largest share of communication-related accidents (42%), with collision and grounding comprising 41% and 24% of incidents. At the consequence level, major repairs are the modal outcome (∼40%), but scenario analysis shows that under highly adverse diversity and proficiency configurations, the combined probability of major repair and total loss can exceed 60%. Strength-of-influence results identify vessel size, nationality, and language diversity as structural drivers (0.54), while language training (0.26), SMCP use (0.24), and cultural familiarization (0.27) are the most effective levers to improve proficiency and reduce conflicts. Scenario analysis further demonstrates that aligning these levers can cut total loss probability on large container ships from about 23% under stressed human-factor conditions to 3% under best-practice communication settings, while shifting incident severity toward minor outcomes and containing casualties. These results provide a quantitative basis for setting language-training thresholds, enforcing SMCP, and designing crew-mix and cultural familiarization policies in maritime safety management.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112358"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Joint optimization of maintenance and spare parts management in upstream–downstream systems under quality control","authors":"Zha Yang ,&nbsp;Ren Lina ,&nbsp;Ding Jiajun ,&nbsp;Kong Xiangyuan","doi":"10.1016/j.ress.2026.112355","DOIUrl":"10.1016/j.ress.2026.112355","url":null,"abstract":"<div><div>This paper studies an upstream–downstream serial production system in which maintenance, quality control, and spare-parts management are commonly optimized in isolation, and proposes a joint optimization strategy to coordinate their coupled dynamics. To sustain production continuity, a three-layer maintenance mechanism is developed: (i) within-lot quality-triggered preventive replacement, (ii) between-lot degradation-driven preventive maintenance, and (iii) post-failure corrective maintenance. For quality assurance and demand buffering, we adopt a “two inspections and one inventory” structure, consisting of an in-process inspection after the upstream stage, a final inspection after the downstream stage, and a capped finished-goods inventory. Spare-parts provisioning is differentiated: the upstream stage is controlled by an (<em>s, S</em>) spare-parts pool, while the downstream stage applies a dual-mode ordering policy conditioned on the real-time finished-goods inventory level. The resulting multi-scenario cost model is optimized using discrete-event simulation, response surface methodology, and a genetic algorithm. Numerical experiments yield an average cost rate of 351.97 $/day, with savings of 7.08 $/day versus Model 1 (without quality control), 10.51 $/day versus Model 2 (without differentiated spare-parts provisioning), and 15.58 $/day versus the baseline Model 3, demonstrating improved coordination and cost-effectiveness.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112355"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174954","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 assessment framework for blockchain performance integration informatics in healthcare industry","authors":"Ahsan Al Nirvik ,&nbsp;Ridwan Mustofa ,&nbsp;Mrinal Kanti Sen ,&nbsp;Niamat Ullah Ibne Hossain","doi":"10.1016/j.ress.2026.112346","DOIUrl":"10.1016/j.ress.2026.112346","url":null,"abstract":"<div><div>Blockchain technology (BC-T) has received increasing attention in healthcare recently for its ability to facilitate medical billing and record management, secure sensitive health information, and support various healthcare applications. However, BC-T is yet to be adopted on a large scale in healthcare industries due to a critical challenge concerning scalability. As the number of nodes and blocks grows, bandwidth decreases and execution times increase, collectively resulting in overall performance degradation. This performance degradation is a multifactor complex phenomenon which is affected by various technical and operational factors. This study first identifies the salient factors that cause performance degradation of healthcare blockchain, then based on them constructs a dynamic Bayesian network (DBN) to model their temporal causal relationships. Finally, DBN is simulated with staged evidence for six-time steps followed by sensitivity analysis and validation to confirm the reliability of the underlying model. The resulting probabilities of performance degradation are measured at about 8.00%, 37.27%, 58.45%, 98.60%, 49.08%, and 9.26% across six-time steps. The proposed methodology could serve as a bluebook on how to assess the performance of healthcare blockchain and offer valuable insights into the salient factors responsible for performance degradation as well as the severity of their impacts. By understanding these dynamics, researchers and practitioners can enhance the reliability and practical usability of blockchain technology, thereby contributing to the modernization of healthcare.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112346"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174920","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":"Multi-stage robust optimization of reliability improvement for power transmission cyber-physical systems under sequential coordinated attacks: an extreme attack scenario search-assisted framework","authors":"Chuangzhi Li,&nbsp;Tianlei Zang,&nbsp;Buxiang Zhou","doi":"10.1016/j.ress.2026.112319","DOIUrl":"10.1016/j.ress.2026.112319","url":null,"abstract":"<div><div>The increasingly deployed information components render the power transmission cyber-physical systems (PTCPS) more vulnerable to cyber-physical attacks (CPAs). Attackers often target critical devices in the communication network, disrupting their operation, with such attacks increasingly exhibiting sequential and coordinated patterns. To address this, we develop a multi-stage robust optimization framework with explicit restoration duration for PTCPS under sequential coordinated CPAs. The framework comprises a defender that hardens components, an attacker that orchestrates coordinated actions, and an operator that executes power redistribution and system restoration. Protection relay intrusions are modeled jointly with deliberate transmission-line outages, and a virtual fault flow representation captures fault propagation across lines, relays, and buses. A key ingredient is an extreme attack scenario search that identifies critical scenarios via objective-free optimization based on fault propagation. The discovered scenarios and constraints are embedded into the master problem, allowing faster convergence of the column-and-constraint generation algorithm and improving the overall efficiency of the multi-stage robust optimization. Case studies on the standard IEEE RTS-96 system demonstrate that the extreme scenario search framework can reduce the overall computational time by approximately 44.8%. Moreover, adding Euclidean distance selection achieves a total reduction of approximately 49.0% in computational time while ensuring stable convergence under coordinated CPAs.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112319"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174929","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":"Wear and rolling contact fatigue problems of locomotive wheels: Mechanisms and countermeasures","authors":"Yunfan Yang ,&nbsp;Xuancheng Yuan ,&nbsp;Ruichen Wang ,&nbsp;Wai Kei Ao ,&nbsp;Liang Ling ,&nbsp;Paul Allen","doi":"10.1016/j.ress.2026.112244","DOIUrl":"10.1016/j.ress.2026.112244","url":null,"abstract":"<div><div>Wear and rolling contact fatigue (RCF) severely deteriorate the tribology behaviour and material integrity of railway wheels, posing significant challenges to their health management. Hence, a deeper mechanistic understanding and the development of effective mitigation strategies are urgently required. In this study, a long-term locomotive wheel wear and RCF evolution prediction model were developed that incorporates the fully nonlinear dynamics of heavy-haul locomotive-track coupled system, the non-Hertzian wheel-rail frictional contact behaviour, and iterative updates of the evolving wear and RCF distributions. The numerical investigations indicated that wear and RCF growth of locomotive wheels are primarily caused by the prominent wheel/rail stresses during curving operations, and particularly aggravated at sharp curves. Subsequent numerical and field investigations verified two effective strategies for mitigating locomotive wheel wear and RCF development: (ⅰ) optimisation design of wheel profile using an innovative constrained multi-object optimisation (CMOO) method, and (ⅱ) enhancement of the Wheel Slide Protection (WSP) controller. The findings further suggested that these two countermeasures can substantially mitigate locomotive wheel wear and RCF progression by lowering wheel-rail tribological interaction and contact stress levels. Overall, this study provides valuable insight into the mechanisms governing wheel wear and RCF evolutions, and supports the enhancement of heavy-haul operational reliability through scientifically informed maintenance practices.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112244"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098487","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":"Network recovery for UAV-assisted IoTs after cascading failures with heterogeneous graph neural networks","authors":"Xiaodian Zhuang ,&nbsp;Xiuwen Fu ,&nbsp;Liudong Xing ,&nbsp;Rui Peng","doi":"10.1016/j.ress.2026.112320","DOIUrl":"10.1016/j.ress.2026.112320","url":null,"abstract":"<div><div>With the growing adoption of unmanned aerial vehicle (UAV)-assisted Internet of Things (IoT), its resilience against cascading failures has garnered significant attention. Cascading failures can severely compromise the topological integrity of such networks, making efficient recovery a significant challenge. To address this challenge, a Network Recovery scheme with Heterogeneous Graph neural network (NRHG) is proposed. The proposed scheme employs a Heterogeneous Graph Neural Network (HGNN), which includes graph perception layers processing local observations from individual UAVs, and graph communication layers enabling information exchange among UAVs. A multi-agent reinforcement learning (MARL) framework is further employed to enable collaborative action decisions for UAVs. Experimental results demonstrate that the proposed NRHG scheme can efficiently schedule surviving UAVs to cover the network blind spots caused by cascading failures. Compared to other schemes, the proposed scheme shows superior performance in both network coverage recovery and system throughput restoration.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112320"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098520","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":"Cause analysis and evolution characteristics of different types of railway accidents based on system dynamics theory","authors":"Taowei Liu ,&nbsp;Mingyi Chen ,&nbsp;Chengde Wang","doi":"10.1016/j.ress.2026.112334","DOIUrl":"10.1016/j.ress.2026.112334","url":null,"abstract":"<div><div>Railway accidents occur frequently worldwide and pose serious risks. On the basis of 547 railway accidents that occurred in the USA, Germany, Japan, and the UK between 2013 and 2023, the key causes and accident evolution characteristics of the railway operation accidents are analyzed using the System Dynamics method. First, the high-frequency accident chains and high-hazard accident chains for the different accidents are determined, and the causal loop diagram and stock–flow diagram of different accidents are established by the system dynamics method. Railway conflict, derailment, and train collision accidents are associated with “human–machine coupling failures”, whereas fire and explosion accidents are related to the operational failures of railway equipment and facilities. Moreover, on the basis of the constructed stock–flow diagram, the evolution of railway accidents can be divided into latent, acceleration, critical and outbreak periods, and each period has unique dominant effects. Finally, a “false safety period” term is defined to reflect the impact of the delayed implementation of control measures on the accumulation of system risks and the evolution of different accidents. The evolution of railway conflict accidents is least affected by the delay effect, whereas that of derailment accidents is most strongly affected.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112334"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175037","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-driven adaptive multi-level pre-optimization control method for reusable launch vehicles under strong stochastic wind disturbances","authors":"Dawen Huang","doi":"10.1016/j.ress.2026.112327","DOIUrl":"10.1016/j.ress.2026.112327","url":null,"abstract":"<div><div>Strong stochastic wind disturbances pose a major threat to the landing reliability of reusable launch vehicles, potentially leading to attitude instability, trajectory deviation, and even structural damage. Traditional control methods, which heavily rely on precise dynamic models and online state observation or prediction, often struggle to ensure reliability under such conditions. To address this challenge, this work proposes a reliability-driven landing pre-optimization method that incorporates regional stochastic wind characteristics, thereby eliminating the need for online observation or prediction. The landing dynamics and stochastic wind field models are established to quantify the destructive impact of winds on landing reliability. An adaptive multi-level control strategy is then introduced, which hierarchically deploys simplified control laws based on real-time altitude and velocity. This design effectively compensates for time-varying wind disturbances without depending on online observers or pre-planned trajectories. Furthermore, a reliability-driven offline optimization framework is developed to tune the control parameters and landing initiation conditions. These key parameters are optimized offline through large-scale Monte Carlo simulations across diverse wind scenarios, thus avoiding the computational burden of online optimization. Finally, the optimal parameters and conditions are pre-optimized to adapt to regional stochastic winds. Results demonstrate that the proposed method achieves a landing reliability of &gt;99.5% and reduces the maximum landing deviation by 99.52%. In comparative studies, the offline pre-optimization method shows superior performance to typical online Model Predictive Control, improving reliability by 14.1%. The proposed strategy offers a robust and practical solution for achieving high-reliability landings under strong stochastic winds.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112327"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146098521","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 novel data-driven health status assessment model based on multiple criteria appraisal recommendation with three-parameter interval grey number","authors":"Long-Hao Yang ,&nbsp;Bei-Ya Qian ,&nbsp;Fei-Fei Ye ,&nbsp;Xiao-Hong Pan ,&nbsp;Haibo Hu ,&nbsp;Haitian Lu","doi":"10.1016/j.ress.2026.112349","DOIUrl":"10.1016/j.ress.2026.112349","url":null,"abstract":"<div><div>Accurate health status assessment is a great challenge when large-scale historical data have accumulated. Hence, this study introduces an advanced framework of multiple criteria appraisal recommendation (MCAR) to develop a data-driven health status assessment model. However, MCAR still fails to extract knowledge from data and represent data uncertainties. To solve these two challenges, the knowledge in the form of IF-THEN rules and three-parameter interval grey number (TPIGN) are used to improve MCAR: 1) the interval rule-base is embedded into MCAR in the aim of extracting IF-THEN rules from data; 2) TPIGN with a new distance is defined to capture data uncertainties in the process of constructing interval rule-base; 3) the interval evidential reasoning (IER) algorithm is served as an inference engine to recommend accurate overall appraisals. Furthermore, on the basis of the improved MCAR, a novel data-driven health status assessment model is proposed by incorporating criterion screening, data preprocessing, activation weight adjustment and risk preference setting. In case study, the effectiveness and superiority of the proposed model are analyzed and verified through the benchmark datasets of lithium-ion batteries and turbofan engines. The comparative results demonstrate the high accuracy and strong robustness of the proposed model comparing with other well-known health status assessment models.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112349"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174755","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 efficient reliability assessment framework for complex mechanism wear degradation based on probabilistic space decomposition kriging model","authors":"Jiang Deyin ,&nbsp;Li Jiahang ,&nbsp;Chen Xingyu ,&nbsp;Ning Yigao ,&nbsp;Jin Yujia ,&nbsp;Liu Jingyi ,&nbsp;Pang Huan","doi":"10.1016/j.ress.2026.112340","DOIUrl":"10.1016/j.ress.2026.112340","url":null,"abstract":"<div><div>Operational wear in complex mechanical systems with joint clearances, driven by nonlinear dynamic behavior, leads to functional degradation and accelerated failure. While critical, the influence of vibration on wear progression is often overlooked in existing studies, and efficiently assessing the reliability of high-fidelity simulation models remains a significant challenge. To address these issues, this paper proposes a reliability assessment framework for wear degradation (PSDK-WDR). First, we develop a non-uniform wear prediction model that integrates a nonlinear dynamic model with the Archard wear model. This model innovatively updates the wear coefficient using parameters derived from real-time vibration signal energy dissipation. Furthermore, for efficient reliability analysis, we introduce a Monte Carlo simulation method based on a Probability Space Decomposition Kriging model (PSDK-MCS). This method decomposes high-dimensional random variables into subsets via Probabilistic Space Decomposition (PSD) and constructs parallel Kriging sub-models for each subset to accelerate assessments. The accuracy and efficiency of the proposed wear model and the PSDK-WDR framework are validated through case studies on crank-slider and flapping-wing mechanisms. This work provides a theoretical basis for understanding wear evolution and efficiently evaluating reliability degradation in complex mechanisms.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"273 ","pages":"Article 112340"},"PeriodicalIF":11.0,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146174766","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}