Mingyuan Liu, Wei He, Ning Ma, Hailong Zhu, Guohui Zhou
{"title":"A new reliability health status assessment model for complex systems based on belief rule base","authors":"Mingyuan Liu, Wei He, Ning Ma, Hailong Zhu, Guohui Zhou","doi":"10.1016/j.ress.2024.110614","DOIUrl":null,"url":null,"abstract":"<div><div>In complex systems, health status assessment identifies system conditions and potential issues. However, large uncertainties and variations make efficient model construction challenging. The belief rule base (BRB), which addresses uncertainty through data-driven and knowledge-driven methods, is widely used for health status assessment of complex systems. Current BRB modeling methods focus primarily on accuracy, leaving a gap in research on reliability. Therefore, a reliable BRB (RE-BRB), which enables effective modeling for complex system health assessment under high reliability requirements, is proposed in this paper. First, a systematic reliability analysis of the BRB is performed, and the reliability criteria are defined. Second, the model parameters of the RE-BRB are optimized via the nondominated sorting whale optimization algorithm with reliability constraints (NSWOA), and the reliability of the model is ensured. In addition, a perturbation analysis of the RE-BRB model is conducted to identify the perturbation thresholds. The perturbation thresholds acceptable to the model provide guidance for managers in making decisions. Last, using the WD615 diesel engine and flywheel bearing as examples, this method achieves reliable system health status assessment by accurately assessing system status, incorporating the ability to address external perturbations and providing an easily interpretable output process.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"254 ","pages":"Article 110614"},"PeriodicalIF":9.4000,"publicationDate":"2024-10-29","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/S0951832024006859","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
In complex systems, health status assessment identifies system conditions and potential issues. However, large uncertainties and variations make efficient model construction challenging. The belief rule base (BRB), which addresses uncertainty through data-driven and knowledge-driven methods, is widely used for health status assessment of complex systems. Current BRB modeling methods focus primarily on accuracy, leaving a gap in research on reliability. Therefore, a reliable BRB (RE-BRB), which enables effective modeling for complex system health assessment under high reliability requirements, is proposed in this paper. First, a systematic reliability analysis of the BRB is performed, and the reliability criteria are defined. Second, the model parameters of the RE-BRB are optimized via the nondominated sorting whale optimization algorithm with reliability constraints (NSWOA), and the reliability of the model is ensured. In addition, a perturbation analysis of the RE-BRB model is conducted to identify the perturbation thresholds. The perturbation thresholds acceptable to the model provide guidance for managers in making decisions. Last, using the WD615 diesel engine and flywheel bearing as examples, this method achieves reliable system health status assessment by accurately assessing system status, incorporating the ability to address external perturbations and providing an easily interpretable output process.
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.