Reliability Engineering & System Safety最新文献

筛选
英文 中文
A human reliability analysis method based on STPA-IDAC and BN-SLIM for driver take-over in Level 3 automated driving 基于 STPA-IDAC 和 BN-SLIM 的人类可靠性分析方法,用于 3 级自动驾驶中的驾驶员接管
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-18 DOI: 10.1016/j.ress.2024.110577
{"title":"A human reliability analysis method based on STPA-IDAC and BN-SLIM for driver take-over in Level 3 automated driving","authors":"","doi":"10.1016/j.ress.2024.110577","DOIUrl":"10.1016/j.ress.2024.110577","url":null,"abstract":"<div><div>Human factors play an important role in the take-over process of Level 3 (L3) automated driving. This paper combines Systems Theoretic Process Analysis (STPA) and Information, Decision and Action in Crew context (IDAC) for qualitative analysis and Bayesian Network (BN) and Success Likelihood Index Method (SLIM) for quantitative calculation to obtain the main performance shaping factors (PSFs) and evaluation indicators that cause human errors. Firstly, the STPA-IDAC method is used to analyze unsafe control actions (UCAs) for take-over process and form the mapping relationship of UCAs-IDA-PSFs. Secondly, the BN of human reliability analysis for take-over process is constructed based on the BN-SLIM method. Uncertainty in rates of PSFs and evaluation indicators is addressed in a probabilistic manner using expert opinions and empirical data. After diagnostic reasoning of BN, mean variation is used to identify the main PSFs and evaluation indicators. This method can effectively identify the main PSFs and evaluation indicators that cause human errors, facilitate risk assessment and management, and reduce the human error probability (HEP).</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527383","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
Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization 基于数字孪生和领域泛化的未知工作条件下的自适应故障诊断
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-18 DOI: 10.1016/j.ress.2024.110560
{"title":"Self-adaptive fault diagnosis for unseen working conditions based on digital twins and domain generalization","authors":"","doi":"10.1016/j.ress.2024.110560","DOIUrl":"10.1016/j.ress.2024.110560","url":null,"abstract":"<div><div>In recent years, intelligent fault diagnosis based on domain adaptation has been used to address domain shifts in cyber–physical systems; however, the need for acquiring target data sufficiently limits their applicability to unseen working conditions. To overcome such limitations, domain generalization techniques have been introduced to enhance the capacity of fault diagnostic models to operate under unseen working conditions. Nevertheless, existing approaches assume access to extensive labeled training data from various source domains, posing challenges in real-world engineering scenarios due to resource constraints. Moreover, the absence of a mechanism for updating diagnostic models over time calls for the exploration of self-adaptive generalized diagnosis models that are capable of autonomous reconfiguration in response to new unseen working conditions. In such a context, this paper proposes a self-adaptive fault diagnosis system that combines several paradigms, namely Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K), Domain Generalization Network Models (DGNMs), and Digital Twins (DT). The MAPE-K loop enables run-time adaptation to dynamic industrial environments without human intervention. To address the scarcity of labeled training data, digital twins are used to generate supplementary data and continuously tune parameters to reflect the dynamics of new unseen working conditions. DGNM incorporates adversarial learning and a domain-based discrepancy metric to enhance feature diversity and generalization. The introduction of multi-domain data augmentation enhances feature diversity and facilitates learning correlations among multiple domains, ultimately improving the generalization of feature representations. The proposed fault diagnosis system has been evaluated on three publicly available rotating machinery datasets to demonstrate its higher performance in cross-work operation and cross-machine tasks compared to other state-of-the-art methods.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527451","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}
引用次数: 0
Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective 智能制造系统对资源故障的鲁棒性分析:双层网络视角
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-18 DOI: 10.1016/j.ress.2024.110595
{"title":"Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective","authors":"","doi":"10.1016/j.ress.2024.110595","DOIUrl":"10.1016/j.ress.2024.110595","url":null,"abstract":"<div><div>Complex and changing environments often cause resource failures in smart manufacturing systems (SMSs), significantly affecting their robustness. This paper introduces a novel methodology to assess the robustness of SMSs facing resource failures, using a complex network approach. It divides SMSs into social and technical layers, analyzes resources and relationships within and between these layers, and establishes a two-layered network model. It also categorizes various types of failures and proposes three robustness metrics to evaluate system performance at individual, local, and global levels. Simulations visually demonstrate the methodology and key findings: (1) the robust-but-fragile trait of SMSs only reacts to node failures and keeps significant in terms of the gradient of robustness; (2) there exists no edge failure that keeps damaging system robustness to the maximal or minimal degrees, and edge failures cause less damage to system robustness than node failures; (3) when failures occur, SMS robustness at all levels changes with inconsistent paces, and the optimal link mode varies by network structures and failure strategies. Finally, managerial implications are presented to guide practical robustness control at different stages of SMS lifecycles.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530261","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
Systematic review and future perspectives on cascading failures in Internet of Things: Modeling and optimization 物联网级联故障的系统回顾与未来展望:建模与优化
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-17 DOI: 10.1016/j.ress.2024.110582
{"title":"Systematic review and future perspectives on cascading failures in Internet of Things: Modeling and optimization","authors":"","doi":"10.1016/j.ress.2024.110582","DOIUrl":"10.1016/j.ress.2024.110582","url":null,"abstract":"<div><div>Under the influence of cascading failures, the failure of a small number of nodes or components may lead to the paralysis of the entire network system. Cascading failures have become one of the major bottlenecks constraining the long-term reliable operation of Internet of Things (IoT) systems, thus attracting extensive attention from researchers. To better understand the complex mechanisms of IoT cascading failures, diverse models and methods have been proposed. This paper systematically reviews the current research status of cascading failures in IoT, covering various aspects such as network objects, performance metrics, failure states, modeling methods, and network optimization. Additionally, we discuss the limitations in current research on cascading failures in IoT and point out the future research directions.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527381","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
Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals 以团队为中心的 IDAC:复杂系统中操作人员的建模和仿真 - 第 1 部分:团队模型和基本原理
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-17 DOI: 10.1016/j.ress.2024.110541
{"title":"Team-centered IDAC: Modeling and simulation of operating crew in complex systems – Part 1: Team model and fundamentals","authors":"","doi":"10.1016/j.ress.2024.110541","DOIUrl":"10.1016/j.ress.2024.110541","url":null,"abstract":"<div><div>Operation of highly complex systems such as Nuclear Power Plants (NPPs) generally require highly trained professional operating teams. Factors associated with teamwork, such as ineffective communication and coordination, can be important contributing factors to accidents and unsafe behavior. The impact of crew interactions on team effectiveness and, consequently, on the entire system, has not been fully and quantitatively explored in high-risk environments such as NPPs. Since a team is an interactive social system, team-specific issues must be studied and evaluated from a “team perspective”—based on team dynamics and processes. This paper is part of a two-papers series that presents a simulation-based Team Model for NPP control room operations. The current paper, Part 1, describes the theoretical fundaments of the model and details its elements. The accompanying paper describes the simulation aspects, and a full application of the method to a pipe break accident in a four- four-loop steam generator feedwater system. The proposed model is based on the IDAC (Information, Decision, and Action in Crew context) cognitive model framework. The resulting model, Team-centered IDAC (Tc-IDAC), examines the team activities “Collaborative information collection,” “Shared decision making,” and “Distributed action execution” through specific modules for Team Error Management. These modules include error detection, error indication and error correction, and team performance shaping factors.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540143","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
Traffic advisory for ship encounter situation based on linear dynamic system 基于线性动态系统的船舶相遇情况交通咨询
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-16 DOI: 10.1016/j.ress.2024.110591
{"title":"Traffic advisory for ship encounter situation based on linear dynamic system","authors":"","doi":"10.1016/j.ress.2024.110591","DOIUrl":"10.1016/j.ress.2024.110591","url":null,"abstract":"<div><div>Enhancing Situation Awareness (SA) is crucial for maritime traffic safety. Various indicators have been developed to assess risks in encounter situations and support the SA of Vessel Traffic Service Operators (VTSOs) and Officers on Watch (OOW), including collision risk and traffic complexity. Despite the widespread use of these navigational aids, ship collision incidents have not been effectively reduced. This paper abstracts ship encounter situations as linear dynamic systems to enhance the understanding of traffic situations. A traffic advisory framework based on the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is proposed by integrating complexity metrics with risk indicators. The proposed method is validated through simulations of head-on, overtaking, and crossing scenarios, demonstrating its ability to accurately assess encounter complexity and issue advisories for free navigation, complexity, and resolution. Finally, we discuss the practical application of the proposed method through real-world experiments conducted in the waters of Qiongzhou Strait. The results indicate that the proposed method effectively quantifies the complexity of ship encounter situations and identifies high-collision-risk vessels from a microscopic perspective while providing insights into maritime traffic surveillance from a macro perspective.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530330","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 sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis 用于滚动轴承故障诊断的声振物理信息融合约束引导深度学习方法
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-15 DOI: 10.1016/j.ress.2024.110556
{"title":"A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis","authors":"","doi":"10.1016/j.ress.2024.110556","DOIUrl":"10.1016/j.ress.2024.110556","url":null,"abstract":"<div><div>Although current deep learning models for bearing fault diagnosis have achieved excellent accuracy, the lack of constraint-guided learning of the physical mechanisms of real bearing failures and a physically scientific training paradigm leads to low interpretability and unreliability of intelligent fault diagnosis models. In this study, a sound-vibration physical-information fusion constraint-guided (PFCG) deep learning (DL) method is proposed, aiming at weighted fusion of sound and vibration multi-physical information into a deep learning model, to guide the DL model to learn more realistic physical laws of bearing failure. Firstly, a 15-degree-of-freedom nonlinear dynamics model of multi-stage degraded bearing failure mechanism with sound-vibration response is developed, which considers the evolutionary mechanism of bearing failure from healthy state to different stages, and utilizes a particle filtering algorithm for dynamic calibration of hidden parameters. Moreover, a lightweight DL fault diagnosis model is designed to realize the deep interaction between the physical model and the DL model through the weighted fusion of the cross-entropy loss function, physical consistency loss and uncertainty loss. Moreover, the superior diagnostic performance of the proposed sound and vibration PFCG-DL model is verified by comparing the performance fluctuations and parameter attributes of different DL benchmark models before and after being guided by physical information fusion constraints (PFCG). Eventually, the proposed PFCG-Transformer model achieves a diagnostic accuracy of 99.45% while keeping the number of parameters at only 0.62M, which significantly improves the accuracy and reduces the computational complexity by 81.5% compared to the CAME-Transformer model's 3.24 M number of parameters and 95.00% diagnostic accuracy. In addition, the test time of PFCG-Transformer is reduced to 1.02 s, which is 60.2% less than CAME-Transformer, demonstrating higher computational efficiency and real-time performance. Importantly, in terms of interpretability, the engineering interpretability and credibility of the models are further improved by visualizing the feature learning results of the vibration modal and multimodal fusion models and the sensitivity analyses of the sound-vibration response models with internal and external physical hyperparameters. Therefore, this study proposes a physical information-guided deep learning method with strong interpretability and superior performance, which provides an important reference for further research and application in the field of bearing fault diagnosis.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530321","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 analysis of load-sharing system with the common-cause failure based on GO-FLOW method 基于 GO-FLOW 方法的共因失效负载分担系统可靠性分析
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-15 DOI: 10.1016/j.ress.2024.110590
{"title":"Reliability analysis of load-sharing system with the common-cause failure based on GO-FLOW method","authors":"","doi":"10.1016/j.ress.2024.110590","DOIUrl":"10.1016/j.ress.2024.110590","url":null,"abstract":"<div><div>The load-sharing system (LSS) with the common-cause failure (CCF) is widely used in industrial engineering applications. If a component in this system fails, the total load is shared by the other components, leading to an increased failure rate of the surviving components. The traditional GO-FLOW method is difficult to calculate the reliability of this system accurately. To address this issue, a new reliability analysis approach is proposed in this paper. In this approach, a new GO-FLOW operator is established to simulate the LSS with CCF. Firstly, the state transfer relationship between components in the LSS is identified. Secondly, the <em>α</em>-factor is used to establish the relationship between the independent failure rate <em>λ<sub>I</sub></em> and the CCF rate <em>λ<sub>C</sub></em>. Finally, the Markov method is employed to calculate the transient-state and steady-state reliability of the system, and the calculation process for the parallel system and k-out-of-n(F) system are given, respectively. The feasibility of the proposed method is illustrated through a numerical example of a distributed electric propulsion system. This approach extends the applicability of the GO-FLOW method.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527448","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 modeling of multi-state phased mission systems with random phase durations and dynamic combined phases 具有随机阶段持续时间和动态组合阶段的多状态分阶段任务系统的可靠性建模
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-15 DOI: 10.1016/j.ress.2024.110524
{"title":"Reliability modeling of multi-state phased mission systems with random phase durations and dynamic combined phases","authors":"","doi":"10.1016/j.ress.2024.110524","DOIUrl":"10.1016/j.ress.2024.110524","url":null,"abstract":"<div><div>Random phase durations and dynamic combined phases challenge the application of existing reliability models in the reliability analysis of multistate-phased mission systems (MS-PMSs). To this end, this paper presents a new reliability modeling method for multi-state phased mission systems with random phase durations and dynamic combined phases. Initially, a multi-state multi-valued decision diagram-based (MMDD-based) reliability modeling method is created to efficiently map random phase durations and the dynamic combined phase nature of MS-PMSs into the reliability model. To solve the MMDD-based reliability model, a path probability evaluation method is subsequently constructed with the assistance of the Markov regenerative function. The effectiveness and the superior performance of the proposed MMDD-based reliability model and its solving algorithm are validated by the application to the reliability modeling and analysis of an attitude and orbit control system with multiple modes. Overall, this paper provides the reliability sector with a new reliability model and its solving algorithm to enhance the reliability and safety of multi-state phased mission systems.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142530323","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}
引用次数: 0
A historical survey of condition-based maintenance models with imperfect inspections: Cases of constant and non-constant probabilities of inspection outcomes 不完全检查的基于状态的维护模型的历史调查:检查结果概率恒定和非恒定的情况
IF 9.4 1区 工程技术
Reliability Engineering & System Safety Pub Date : 2024-10-12 DOI: 10.1016/j.ress.2024.110544
{"title":"A historical survey of condition-based maintenance models with imperfect inspections: Cases of constant and non-constant probabilities of inspection outcomes","authors":"","doi":"10.1016/j.ress.2024.110544","DOIUrl":"10.1016/j.ress.2024.110544","url":null,"abstract":"<div><div>This article offers an extensive historical review of condition-based maintenance (CBM) models, focusing on the impact of imperfect inspections. It examines the progression and development of CBM models that incorporate both constant and non-constant probabilities of inspection outcomes. The review encompasses early foundational work, significant theoretical advancements, and practical applications across diverse industries. It investigates how different assumptions about inspection accuracy and failure detection impact CBM cost, system availability and operational reliability. Moreover, the article highlights methodological innovations that address the challenges posed by imperfect inspections, such as probabilistic modeling and optimization techniques. This survey aims to provide a thorough understanding of the complexities in CBM modeling and offers insights for future research to improve maintenance decision-making under inspection uncertainty.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":null,"pages":null},"PeriodicalIF":9.4,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142527382","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
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信