Development of a Bayesian multi-state degradation model for up-to-date reliability estimations of working industrial components

• Published in: Reliability engineering & system safety 2017-10, Vol.166, p.25-40
• Main Authors: Compare, M., Baraldi, P., Bani, I., Zio, E., Mc Donnell, D.
• Format: Article
• Language: English
• Subjects: Bayesian analysis; Bayesian inference; Biopharmaceuticals; Case studies; Component reliability; Computer simulation; Data processing; Degradation; Diaphragms; Industrial equipment; Maintenance; Markov analysis; Markov chains; Markov processes; MCMC algorithms; Multi-state degradation modeling; Reliability engineering; Remaining useful life; Statistics; System reliability; Upgrading; Useful life; Weibull distribution
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2016.11.020

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. •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.