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Stochastic Accelerated Degradation Models Based on a Generalized Cumulative Damage Approach

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Statistical Modeling for Degradation Data

Part of the book series: ICSA Book Series in Statistics ((ICSABSS))

Abstract

A general methodology for stochastic degradation models is introduced that allows for both hard and soft failures to be taken into account when conducting parametric inference on lifetimes. Due to the development of engineering and science technology, modern products have longer lifetimes and greater reliability than ever before. Thus, it often takes more time to observe failures under normal-use conditions. Accelerated tests have been developed in order to deal with this lifetime-to-failure increase. Accelerated tests decrease the strength or lifetime to failure by exposing the specimens or products to harsh conditions. This exposure results in earlier breakdowns. Modelling these accelerated tests requires the use of stochastic degradation models with accelerating explanatory variables. By using a generalized cumulative damage approach with a stochastic process describing degradation, we develop stochastic accelerated degradation models which handle failure data consisting of both hard and soft failures.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (No. NRF-2017R1A2B4004169). We appreciate the valuable comments from anonymous referees which led to an improvement of the article. The author also wishes to dedicate this work to the memory and honor of Professor Byung Ho Lee in the Department of Nuclear Engineering at Seoul National University.

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Authors and Affiliations

  1. Department of Industrial Engineering, Pusan National University, 46241, Busan, South Korea

    Chanseok Park

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  1. Chanseok Park
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Correspondence to Chanseok Park .

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Editors and Affiliations

  1. School of Social Work & Department of Biostatistics, Department of Statistics, University of North Carolina, University of Pretoria, Chapel Hill, North Carolina, USA

    Ding-Geng (Din) Chen

  2. Department of Mathematical Sciences, University of South Dakota, Vermillion, South Dakota, USA

    Yuhlong Lio

  3. Department of Statistical Science, Southern Methodist University, Dallas, Texas, USA

    Hon Keung Tony Ng

  4. Department of Statistics, Tamkang University, New Taipei City, Taiwan

    Tzong-Ru Tsai

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Park, C. (2017). Stochastic Accelerated Degradation Models Based on a Generalized Cumulative Damage Approach. In: Chen, DG., Lio, Y., Ng, H., Tsai, TR. (eds) Statistical Modeling for Degradation Data. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-5194-4_1

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