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http://dx.doi.org/10.14346/JKOSOS.2021.36.2.32

A Study on Bayesian Reliability Evaluation of IPM using Simple Information  

Jo, Dong Cheol (Department of Rolling Stock System Engineering, Seoul National University of Science & Technology)
Koo, Jeong Seo (Department of railway Safety Engineering, Seoul National University of Science & Technology)
Publication Information
Journal of the Korean Society of Safety / v.36, no.2, 2021 , pp. 32-38 More about this Journal
Abstract
This paper suggests an approach to evaluate the reliability of an intelligent power module with information deficiency of prior distribution and the characteristics of censored data through Bayesian statistics. This approach used a prior distribution of Bayesian statistics using the lifetime information provided by the manufacturer and compared and evaluated diffuse prior (vague prior) distributions. To overcome the computational complexity of Bayesian posterior distribution, it was computed with Gibbs sampling in the Monte Carlo simulation method. As a result, the standard deviation of the prior distribution developed using simple information was smaller than that of the posterior distribution calculated with the diffuse prior. In addition, it showed excellent error characteristics on RMSE compared with the Kaplan-Meier method.
Keywords
IPM; bayesian reliability; gibbs sampling; RMSE; Kaplan-Meier;
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