{"title":"Robustness analysis of smart manufacturing systems against resource failures: A two-layered network perspective","authors":"","doi":"10.1016/j.ress.2024.110595","DOIUrl":null,"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":9.4000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reliability Engineering & System Safety","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0951832024006665","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, INDUSTRIAL","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.