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Sample selection of prognostics validation test based on multi-stage Wiener process

Author

Listed:
  • Zhiao Zhao
  • Yong Zhang
  • Guanjun Liu
  • Jing Qiu

Abstract

Sample allocation and selection technology is of great significance in the test plan design of prognostics validation. Considering the existing researches, the importance of prognostics samples of different moments is not considered in the degradation process of a single failure. Normally, prognostics samples are generated under the same time interval mechanism. However, a prognostics system may have low prognostics accuracy because of the small quantity of failure degradation and measurement randomness in the early stage of a failure degradation process. Historical degradation data onto equipment failure modes are collected, and the degradation process model based on the multi-stage Wiener process is established. Based on the multi-stage Wiener process model, we choose four parameters to describe different degradation stages in a degradation process. According to four parameters, the sample selection weight of each degradation stage is calculated and the weight of each degradation stage is used to select prognostics samples. Taking a bearing wear fault of a helicopter transmission device as an example, its degradation process is established and sample selection weights are calculated. According to the sample selection weight of each degradation process, we accomplish the prognostics sample selection of the bearing wear fault. The results show that the prognostics sample selection method proposed in this article has good applicability.

Suggested Citation

  • Zhiao Zhao & Yong Zhang & Guanjun Liu & Jing Qiu, 2019. "Sample selection of prognostics validation test based on multi-stage Wiener process," Journal of Risk and Reliability, , vol. 233(4), pages 605-614, August.
  • Handle: RePEc:sae:risrel:v:233:y:2019:i:4:p:605-614
    DOI: 10.1177/1748006X18805835
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    1. Bhattacharya, Ritwik & Pradhan, Biswabrata & Dewanji, Anup, 2015. "Computation of optimum reliability acceptance sampling plans in presence of hybrid censoring," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 91-100.
    2. van Noortwijk, J.M., 2009. "A survey of the application of gamma processes in maintenance," Reliability Engineering and System Safety, Elsevier, vol. 94(1), pages 2-21.
    3. Wang, Xiao, 2010. "Wiener processes with random effects for degradation data," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 340-351, February.
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