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Journal of Mechanical Science and Technology

, Volume 33, Issue 11, pp 5425–5435 | Cite as

Stochastic accelerated degradation model involving multiple accelerating variables by considering measurement error

  • Junxing Li
  • Zhihua WangEmail author
  • Chengrui Liu
  • Ming Qiu
Article
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Abstract

In accelerated degradation tests, products are usually exposed to several environmental variables or operating conditions. This motivates the need for developing an accelerated degradation model involving multiple accelerating variables. Among the current literature, the conventional accelerated degradation models involving multiple accelerating variables have not considered the measurement error, which inevitably exists in practical degradation datasets. Therefore, a Wiener process-based accelerated degradation model that simultaneously considering multiple accelerating variables and measurement error is proposed. Then approximate closed-form expressions for the failure time distribution (FTD) and its percentiles are derived. The expectation maximization (EM) algorithm is adopted to estimate unknown parameters. Moreover, a multivariate normality testing method is developed to test the fitting goodness of the degradation model. Finally, a comprehensive simulation study and a real application are given to validate the proposed method. The result shows that the proposed model can provide precise estimates even for small sample size of approximately five, and the estimated mean square errors (MSEs) of the mean time to failure (MTTF) and the FTD percentile of the proposed model can be improved by at least 70 % compared with those of the reference methods when the sample size is same.

Keywords

Accelerated degradation analysis Multiple accelerating variables Measurement error Wiener process EM algorithm 

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Notes

Acknowledgments

The authors are grateful to the anonymous reviewers, and the editor, for their critical and constructive review of the manuscript. This study was supported by the National Basic Research Program of China (Grant No. 2018YFB2000203), National Natural Science Foundation of China (Grant No. 11872085) and Key Scientific Research Projects of Henan Colleges and Universities of China (Grant No. 19A460002).

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Copyright information

© KSME & Springer 2019

Authors and Affiliations

  • Junxing Li
    • 1
  • Zhihua Wang
    • 2
    Email author
  • Chengrui Liu
    • 3
  • Ming Qiu
    • 1
  1. 1.School of Mechatronical EngineeringHenan University of Science and TechnologyLuoyangChina
  2. 2.School of Aeronautic Science and EngineeringBeihang UniversityBeijingChina
  3. 3.Beijing Institute of Control EngineeringBeijingChina

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