Optimal design of step-stress accelerated degradation tests based on genetic algorithm and neural network
In this study, the optimal design of step-stress accelerated degradation tests is focused. An optimization model is proposed where an improved accelerated degradation model is involved to comprehensively consider the influence of accelerated stress and the measurement error. Then, a novel optimal de...
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| Published in | Quality engineering Vol. 36; no. 1; pp. 66 - 79 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Milwaukee
Taylor & Francis
02.01.2024
Taylor & Francis Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0898-2112 1532-4222 |
| DOI | 10.1080/08982112.2023.2225583 |
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| Abstract | In this study, the optimal design of step-stress accelerated degradation tests is focused. An optimization model is proposed where an improved accelerated degradation model is involved to comprehensively consider the influence of accelerated stress and the measurement error. Then, a novel optimal design method is constructed, where multiple decision variables can be simultaneously optimized based on neural network and genetic algorithm. An effective sensitivity analysis method is further established to quantitively illustrate the influence of the predetermined model parameters on the optimal results. Finally, a case study is implemented, and a series of comparisons are implemented to demonstrate the effectiveness and rationality of the proposed method. |
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| AbstractList | In this study, the optimal design of step-stress accelerated degradation tests is focused. An optimization model is proposed where an improved accelerated degradation model is involved to comprehensively consider the influence of accelerated stress and the measurement error. Then, a novel optimal design method is constructed, where multiple decision variables can be simultaneously optimized based on neural network and genetic algorithm. An effective sensitivity analysis method is further established to quantitively illustrate the influence of the predetermined model parameters on the optimal results. Finally, a case study is implemented, and a series of comparisons are implemented to demonstrate the effectiveness and rationality of the proposed method. |
| Author | Wang, Zhihua Bao, Rui Mao, Zelong Ren, Kunpeng Liu, Gen |
| Author_xml | – sequence: 1 givenname: Gen surname: Liu fullname: Liu, Gen organization: School of Aeronautic Science and Engineering, Beihang University – sequence: 2 givenname: Zhihua surname: Wang fullname: Wang, Zhihua organization: School of Aeronautic Science and Engineering, Beihang University – sequence: 3 givenname: Rui surname: Bao fullname: Bao, Rui organization: School of Aeronautic Science and Engineering, Beihang University – sequence: 4 givenname: Zelong surname: Mao fullname: Mao, Zelong organization: Navigation Instrument Research Institute – sequence: 5 givenname: Kunpeng surname: Ren fullname: Ren, Kunpeng organization: Navigation Instrument Research Institute |
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| Cites_doi | 10.1016/j.fss.2011.08.011 10.1080/07408170490507701 10.21629/JSEE.2017.05.21 10.1109/72.97934 10.1109/TR.2005.863811 10.1109/RAMS.2011.5754481 10.1109/JSEE.2015.00058 10.1080/08982112.2017.1307391 10.1007/s10898-018-0629-y 10.4028/www.scientific.net/AMM.157-158.1595 10.1080/02664760903406488 10.1080/08982112.2018.1545238 10.1109/ACCESS.2017.2683533 10.1002/asmb.2332 10.1002/qre.1489 10.1080/08982112.2020.1755686 10.1080/07408170802322697 10.1016/j.microrel.2021.114334 10.1126/science.aaa8415 10.1109/ICRMS.2009.5270017 10.1109/RAMS.2004.1285469 10.1016/j.enconman.2007.06.002 10.7232/iems.2017.16.2.155 |
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| SubjectTerms | Accelerated tests Degradation Design optimization Error analysis genetic algorithm Genetic algorithms multiple decision variables Neural networks optimal design Optimization models proxy model Sensitivity analysis step-stress accelerated degradation test |
| Title | Optimal design of step-stress accelerated degradation tests based on genetic algorithm and neural network |
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