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Degradation Analysis with Measurement Errors

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Proceedings of the Pacific Rim Statistical Conference for Production Engineering

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

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Abstract

The lifetime information for highly reliable products is usually assessed by a degradation model. When there are measurement errors in monotonic degradation paths, non-monotonic model assumption can lead to contradictions between physical/chemical mechanisms and statistical explanations. To settle the contradiction, this study presents an independent increment degradation-based process that simultaneously considers the unit-to-unit variability, the within-unit variability, and the measurement error in the degradation data. Several case studies show the flexibility and applicability of the proposed models. This paper also uses a separation-of-variables transformation with a quasi-Monte Carlo method to estimate the model parameters. A degradation diagnostic is provided to evaluate the validity of model assumptions.

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Acknowledgements

This work was supported by the Ministry of Science and Technology (Grant No: MOST-104-2118-M-001-007) of Taiwan, Republic of China. The authors would like to thank Ms. Ya-Shan Cheng for her assistance in the computations.

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Authors and Affiliations

  1. Institute of Statistical Science, Academia Sinica, Taipei, Taiwan

    Chien-Yu Peng & Hsueh-Fang Ai

Authors
  1. Chien-Yu Peng
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  2. Hsueh-Fang Ai
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Corresponding author

Correspondence to Chien-Yu Peng .

Editor information

Editors and Affiliations

  1. Oregon Health & Science University , Portland, Oregon, USA

    Dongseok Choi

  2. Pukyong National University , Busan, Korea (Republic of)

    Daeheung Jang

  3. Stanford University, Stanford, California, USA

    Tze Leung Lai

  4. Seoul National University, Gwanak-gu, Seoul, Korea (Republic of)

    Youngjo Lee

  5. Stanford University , Stanford, California, USA

    Ying Lu

  6. University of Michigan–Ann Arbor, Ann Arbor, Michigan, USA

    Jun Ni

  7. University of Wisconsin–Madison, Madison, Wisconsin, USA

    Peter Qian

  8. University of Florida, Gainesville, Florida, USA

    Peihua Qiu

  9. University of Chicago , Chicago, Illinois, USA

    George Tiao

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Peng, CY., Ai, HF. (2018). Degradation Analysis with Measurement Errors. In: Choi, D., et al. Proceedings of the Pacific Rim Statistical Conference for Production Engineering. ICSA Book Series in Statistics. Springer, Singapore. https://doi.org/10.1007/978-981-10-8168-2_8

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