A Bayesian framework for reliability assessment via Wiener process and MCMC
type: Item
instance of: scholarly article
title: A Bayesian framework for reliability assessment via Wiener process and MCMC
zbMATH Open document ID: 1407.62365
DOI: 10.1155/2014/486368
author: Huibing Hao
author: Chun Su
published in: Mathematical Problems in Engineering
publication date: 2019-02-08T00:00:00Z
review text: Summary: The population and individual reliability assessment are discussed, and a Bayesian framework is proposed to integrate the population degradation information and individual degradation data. Different from fixed effect Wiener process modeling, the population degradation path is characterized by a random effect Wiener process, and the model can capture sources of uncertainty including unit to unit variation and time correlated structure. Considering that the model is so complicated and analytically intractable, Markov Chain Monte Carlo (MCMC) method is used to estimate the unknown parameters in the population model. To achieve individual reliability assessment, we exploit a Bayesian updating method, by which the unknown parameters are updated iteratively. Based on updated results, the residual use life and reliability evaluation are obtained. A lasers data example is given to demonstrate the usefulness and validity of the proposed model and method.
Mathematics Subject Classification ID: 62N05
Mathematics Subject Classification ID: 62F15
Mathematics Subject Classification ID: 90B25
zbMATH DE Number: 7016551
Wikidata QID: Q59067627
describes a project that uses: WinBUGS
describes a project that uses: SPLIDA
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/486368
OpenAlex ID: W2090922313
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description: scientific article; zbMATH DE number 7016551