Reliability evaluation based on stochastic degradation models with a new model selection criterion

Songhua Hao; Wer Chen

doi:10.1117/12.2686149

28 July 2023 Reliability evaluation based on stochastic degradation models with a new model selection criterion

Songhua Hao, Wer Chen

Proceedings Volume 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023); 127562T (2023) https://doi.org/10.1117/12.2686149
Event: 2023 3rd International Conference on Applied Mathematics, Modelling and Intelligent Computing (CAMMIC 2023), 2023, Tangshan, China

Abstract

To capture the inherent temporal variability of the degradation process over time, stochastic degradation models have become a popular topic of reliability evaluation for complex systems with high reliability and long lifetime. For a practical degrading product, several stochastic degradation models may be a candidate for its reliability modelling, and it is significant to measure their incompleteness and construct an effective model selection criterion. Traditional model selection criterion mainly emphasizes the balance between model complexity and degradation data fitting effect, but the extrapolation and prediction effect of the product reliability model under accelerated degradation test is limited. Therefore, based on the accelerated degradation test and long-term degradation data of a real product, this paper adapts the traditional Bayesian Information Criterion and proposes a new model selection criterion. By balancing model complexity, data fitting effect and prediction effect, the optimal selection of the Wiener degradation process, gamma degradation process and inverse Gaussian degradation process can be realized, and we can obtain more accurate reliability evaluation and lifetime prediction results.

Citation Download Citation

Songhua Hao and Wer Chen "Reliability evaluation based on stochastic degradation models with a new model selection criterion", Proc. SPIE 12756, 3rd International Conference on Applied Mathematics, Modelling, and Intelligent Computing (CAMMIC 2023), 127562T (28 July 2023); https://doi.org/10.1117/12.2686149

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