Abstract
Prediction of remaining useful life using the field monitored performance data provides a more realistic estimate of life and helps develop a better asset management plan. The field performance can be monitored (indirectly) by observing the degradation of the quality characteristics of a product. This paper considers the gamma process to model the degradation behavior of the product characteristics. An integrated Bayesian approach is proposed to estimate the remaining useful life that considers accelerated degradation data to model degradation behavior first. The proposed approach also considers interaction effects in a multi-stress scenario impacting the degradation process. To reduces the computational complexity, posterior distributions are estimated using the MCMC simulation technique. The proposed method has been demonstrated with an LED case example and results show the superiority of Bayesian-based RUL estimation.
Highlights
Most of the recently designed engineering products and systems are highly reliable especially in the automotive, aerospace, electronics, and military industries
Remaining Useful Life Model 2.1 Degradation Modeling The degradation of quality characteristics behaves probabilistically over time
In this work, the remaining useful life (RUL) prediction of highly reliable products has been proposed using the integration of accelerated degradation testing (ADT) data and the Bayesian inference method
Summary
Most of the recently designed engineering products and systems are highly reliable especially in the automotive, aerospace, electronics, and military industries. The majority of the physical degradation processes such as wear, fatigue, corrosion are nonnegative and monotonic degradation This makes the gamma stochastic process a natural choice to model the degradation behavior for RUL prediction. Ling et al (2019) presented a RUL estimation work considering different phases of degradation behavior with Bayesian inference. To address the research issues discussed earlier, this paper proposes a gamma process-based RUL model considering multi-stress factors with the interaction effect and stress-depended gamma parameters. Remaining Useful Life Model 2.1 Degradation Modeling The degradation of quality characteristics behaves probabilistically over time This uncertain behavior can be best suited to a model with well-known stochastic processes such as the Wiener process, gamma process, and inverse Gaussian process. Using equation (6) the expected value of RUL can be written as:
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