Prognostics and system health management becomes an important topic in modern reliability study. In prognostics and system health management, remaining useful life is one of the vital indexes to yield an advance warning of impending failure in a system, thereby helping in executing preventive actions prior to failure occurrence and helping in making maintenance decisions. To obtain precise statistical inference on the remaining useful life, we consider degradation models that incorporate unit-specific random effects that model heterogeneity in the degradation of distinct systems, and propose a parametric bootstrap confidence interval for the remaining useful life of each system. A Monte Carlo simulation study is carried out to evaluate the performance of the proposed methodology. To illustrate the suggested model and inferential methods, a real data set of light intensity of light emitting diodes is analyzed. Copyright © 2017 Springer Nature Singapore Pte Ltd.
|Title of host publication||Statistical modeling for degradation data|
|Editors||Ding-Geng (Din) CHEN, Yuhlong LIO, Hon Keung Tony NG, Tzong-Ru TSAI|
|Place of Publication||Singapore|
|Publication status||Published - 2017|
CitationLing, M. H., Ng, H. K. T., & Tsui, K.-L. (2017). Inference on remaining useful life under gamma degradation models with random effects. In D.-G. Chen, Y. Lio, H. K. T. Ng, & T.-R. Tsai (Eds.), Statistical modeling for degradation data (pp. 253-266). Singapore: Springer.
- Degradation models
- Gamma process
- Random effects
- Remaining useful life
- Maximum likelihood estimation