Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components. (October 2017)

• Record Type: Journal Article
• Authors: Compare, M.; Baraldi, P.; Bani, I.; Zio, E.; Mc Donnell, D.
• Is Part Of: Reliability engineering & system safety. Volume 166(2017)
• Page Start: 25
• Page End: 40
• Publication Date: 2017-10
• Subjects: A-MCMC Adaptive MCMC algorithm -- CDF Cumulative Distribution Function -- EPDM Ethylene Propylene Diene Monomer -- i.i.d independent and identically distributed -- MCMC Markov Chain Monte Carlo -- MTS Most Trustworthy Specification -- N-RWMH Normal Random Walk Metropolis-Hastings algorithm -- PDF Probability Density Function -- RUL Remaining Useful Life; Multi-state degradation modeling -- Weibull distribution -- Remaining useful life -- Maintenance -- Bayesian inference -- MCMC algorithms; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2016.11.020
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: We consider a three-state continuous-time semi-Markov process with Weibull-distributed transition times to model the degradation mechanism of an industrial equipment. To build this model, an original combination of techniques is proposed for building a semi-Markov degradation model based on expert knowledge and few field data within the Bayesian statistical framework. The issues addressed are: i) the prior elicitation of the model parameters values from experts, avoiding possible information commitment; ii) the development of a Markov-Chain Monte Carlo algorithm for sampling from the posterior distribution; iii) the posterior inference of the model parameters values and, on this basis, the estimation of the time-dependent state probabilities and the prediction of the equipment remaining useful life. The developed Bayesian model offers the possibility of updating the system reliability estimation every time a new evidence is gathered. The application of the modeling framework is illustrated by way of a real industrial case study concerning the degradation of diaphragms installed in a production line of a biopharmaceutical industry. Highlights: Equipment degradation is modeled as a three-state semi-Markov process with Weibull-distributed transition times. Bayesian statistics is used to combine expert knowledge and field data for parameter estimation. A Markov-Chain Monte Carlo algorithm is developed for sampling from the posterior distribution. The developed model allows estimating the time-dependent state probabilities and the equipment RUL. The developed model allows updating the reliability estimation every time a new evidence is gathered.