{"title":"多级验收不确定条件下可靠性论证的优化试验设计","authors":"Bingjie Wang, Lu Lu, Suiyao Chen, Mingyang Li","doi":"10.1080/08982112.2023.2249188","DOIUrl":null,"url":null,"abstract":"AbstractA reliability demonstration test (RDT) plays a critical role in safeguarding product reliability and making sure it meets the target requirement. When planning an RDT, the test planning parameters are determined before executing the RDT. There is uncertainty associated with the test result and whether the product will be acceptable and released into the market with additional costs resulting from the warranty service or whether a reliability growth process is needed to further improve the product’s reliability. Potentially, such a process could be repeated multiple times depending on how quickly the reliability growth process can improve product reliability. Existing RDT designs primarily consider the cost of RDT itself or over a single demonstration stage before the next possible RDT, and hence fail to fully address the uncertainty of all possible future RDTs and various pathways a product may go through in a multi-stage demonstration process. By focusing on binomial RDT (BRDT) plans based on failure count data, this paper proposes an optimal Bayesian BRDT design framework by explicitly quantifying the multi-stage acceptance uncertainties resulting from current and subsequent BRDTs. It allows the BRDT planning decision to be determined more holistically by anticipating the costs of warranty service and reliability growth along different pathways over multiple stages. A recursive information propagation algorithm is proposed to incorporate the prior belief of product reliability and allow it to evolve and update over multiple stages of BRDT. A case study is presented to illustrate the proposed multi-stage Bayesian BRDT design framework and demonstrate its cost-efficiency compared to existing strategies. A comprehensive sensitivity analysis is also provided to demonstrate the impact of the relative size of different cost components, reliability growth rate, and prior setting on the performance of the proposed method.Keywords: bayesian reliabilityinformation propagationmulti-stage uncertaintiesoptimal test designreliability demonstration test Additional informationNotes on contributorsBingjie WangBingjie Wang is a PhD student in the Department of Industrial & Management Systems Engineering at the University of South Florida. She received her MS in Industrial Engineering from the State University of New York at Buffalo. Her research interests include decision science, data science and AI techniques.Lu LuLu Lu is an Associate Professor of Statistics in the Department of Mathematics and Statistics at the University of South Florida. She was a postdoctoral research associated in the Statistics Sciences Group at Los Alamos National Laboratory. She earned a doctorate in Statistics from Iowa State University. Her research interests include reliability analysis, design of experiments, response surface methodology, survey sampling, multiple objective/response optimization. She is a member of the American Statistical Association and the American Society for Quality.Suiyao ChenSuiyao Chen is Data Scientist at Amazon. He received his PhD in the Department of Industrial and Management Systems Engineering at the University of South Florida. He also received his MA degree in Statistics from Columbia University. His research interests include reliability demonstration tests, Bayesian data analytics and warranty analysis.Mingyang LiMingyang Li is an Associate Professor in the Department of Industrial & Management Systems Engineering at the University of South Florida. He received his PhD in Systems & Industrial Engineering and MS in Statistics from the University of Arizona. He also received a MS in Mechanical & Industrial Engineering from the University of Iowa. His research interests include reliability and quality assurance, Bayesian data analytics and system informatics. Dr. Li is a member of INFORMS, IISE and ASQ.","PeriodicalId":20846,"journal":{"name":"Quality Engineering","volume":"54 1","pages":"0"},"PeriodicalIF":1.3000,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Optimal test design for reliability demonstration under multi-stage acceptance uncertainties\",\"authors\":\"Bingjie Wang, Lu Lu, Suiyao Chen, Mingyang Li\",\"doi\":\"10.1080/08982112.2023.2249188\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"AbstractA reliability demonstration test (RDT) plays a critical role in safeguarding product reliability and making sure it meets the target requirement. When planning an RDT, the test planning parameters are determined before executing the RDT. There is uncertainty associated with the test result and whether the product will be acceptable and released into the market with additional costs resulting from the warranty service or whether a reliability growth process is needed to further improve the product’s reliability. Potentially, such a process could be repeated multiple times depending on how quickly the reliability growth process can improve product reliability. Existing RDT designs primarily consider the cost of RDT itself or over a single demonstration stage before the next possible RDT, and hence fail to fully address the uncertainty of all possible future RDTs and various pathways a product may go through in a multi-stage demonstration process. By focusing on binomial RDT (BRDT) plans based on failure count data, this paper proposes an optimal Bayesian BRDT design framework by explicitly quantifying the multi-stage acceptance uncertainties resulting from current and subsequent BRDTs. It allows the BRDT planning decision to be determined more holistically by anticipating the costs of warranty service and reliability growth along different pathways over multiple stages. A recursive information propagation algorithm is proposed to incorporate the prior belief of product reliability and allow it to evolve and update over multiple stages of BRDT. A case study is presented to illustrate the proposed multi-stage Bayesian BRDT design framework and demonstrate its cost-efficiency compared to existing strategies. A comprehensive sensitivity analysis is also provided to demonstrate the impact of the relative size of different cost components, reliability growth rate, and prior setting on the performance of the proposed method.Keywords: bayesian reliabilityinformation propagationmulti-stage uncertaintiesoptimal test designreliability demonstration test Additional informationNotes on contributorsBingjie WangBingjie Wang is a PhD student in the Department of Industrial & Management Systems Engineering at the University of South Florida. She received her MS in Industrial Engineering from the State University of New York at Buffalo. Her research interests include decision science, data science and AI techniques.Lu LuLu Lu is an Associate Professor of Statistics in the Department of Mathematics and Statistics at the University of South Florida. She was a postdoctoral research associated in the Statistics Sciences Group at Los Alamos National Laboratory. She earned a doctorate in Statistics from Iowa State University. Her research interests include reliability analysis, design of experiments, response surface methodology, survey sampling, multiple objective/response optimization. She is a member of the American Statistical Association and the American Society for Quality.Suiyao ChenSuiyao Chen is Data Scientist at Amazon. He received his PhD in the Department of Industrial and Management Systems Engineering at the University of South Florida. He also received his MA degree in Statistics from Columbia University. His research interests include reliability demonstration tests, Bayesian data analytics and warranty analysis.Mingyang LiMingyang Li is an Associate Professor in the Department of Industrial & Management Systems Engineering at the University of South Florida. He received his PhD in Systems & Industrial Engineering and MS in Statistics from the University of Arizona. He also received a MS in Mechanical & Industrial Engineering from the University of Iowa. His research interests include reliability and quality assurance, Bayesian data analytics and system informatics. 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Optimal test design for reliability demonstration under multi-stage acceptance uncertainties
AbstractA reliability demonstration test (RDT) plays a critical role in safeguarding product reliability and making sure it meets the target requirement. When planning an RDT, the test planning parameters are determined before executing the RDT. There is uncertainty associated with the test result and whether the product will be acceptable and released into the market with additional costs resulting from the warranty service or whether a reliability growth process is needed to further improve the product’s reliability. Potentially, such a process could be repeated multiple times depending on how quickly the reliability growth process can improve product reliability. Existing RDT designs primarily consider the cost of RDT itself or over a single demonstration stage before the next possible RDT, and hence fail to fully address the uncertainty of all possible future RDTs and various pathways a product may go through in a multi-stage demonstration process. By focusing on binomial RDT (BRDT) plans based on failure count data, this paper proposes an optimal Bayesian BRDT design framework by explicitly quantifying the multi-stage acceptance uncertainties resulting from current and subsequent BRDTs. It allows the BRDT planning decision to be determined more holistically by anticipating the costs of warranty service and reliability growth along different pathways over multiple stages. A recursive information propagation algorithm is proposed to incorporate the prior belief of product reliability and allow it to evolve and update over multiple stages of BRDT. A case study is presented to illustrate the proposed multi-stage Bayesian BRDT design framework and demonstrate its cost-efficiency compared to existing strategies. A comprehensive sensitivity analysis is also provided to demonstrate the impact of the relative size of different cost components, reliability growth rate, and prior setting on the performance of the proposed method.Keywords: bayesian reliabilityinformation propagationmulti-stage uncertaintiesoptimal test designreliability demonstration test Additional informationNotes on contributorsBingjie WangBingjie Wang is a PhD student in the Department of Industrial & Management Systems Engineering at the University of South Florida. She received her MS in Industrial Engineering from the State University of New York at Buffalo. Her research interests include decision science, data science and AI techniques.Lu LuLu Lu is an Associate Professor of Statistics in the Department of Mathematics and Statistics at the University of South Florida. She was a postdoctoral research associated in the Statistics Sciences Group at Los Alamos National Laboratory. She earned a doctorate in Statistics from Iowa State University. Her research interests include reliability analysis, design of experiments, response surface methodology, survey sampling, multiple objective/response optimization. She is a member of the American Statistical Association and the American Society for Quality.Suiyao ChenSuiyao Chen is Data Scientist at Amazon. He received his PhD in the Department of Industrial and Management Systems Engineering at the University of South Florida. He also received his MA degree in Statistics from Columbia University. His research interests include reliability demonstration tests, Bayesian data analytics and warranty analysis.Mingyang LiMingyang Li is an Associate Professor in the Department of Industrial & Management Systems Engineering at the University of South Florida. He received his PhD in Systems & Industrial Engineering and MS in Statistics from the University of Arizona. He also received a MS in Mechanical & Industrial Engineering from the University of Iowa. His research interests include reliability and quality assurance, Bayesian data analytics and system informatics. Dr. Li is a member of INFORMS, IISE and ASQ.
期刊介绍:
Quality Engineering aims to promote a rich exchange among the quality engineering community by publishing papers that describe new engineering methods ready for immediate industrial application or examples of techniques uniquely employed.
You are invited to submit manuscripts and application experiences that explore:
Experimental engineering design and analysis
Measurement system analysis in engineering
Engineering process modelling
Product and process optimization in engineering
Quality control and process monitoring in engineering
Engineering regression
Reliability in engineering
Response surface methodology in engineering
Robust engineering parameter design
Six Sigma method enhancement in engineering
Statistical engineering
Engineering test and evaluation techniques.