Repairable system analysis in presence of covariates and random effects

• Published in: Reliability engineering & system safety Vol. 131; pp. 271 - 281
• Main Authors: Giorgio, M., Guida, M., Pulcini, G.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Oxford Elsevier Ltd 01.11.2014; Elsevier
• Subjects: Applied sciences; Bayes inference; Bayesian analysis; Buses; Drives; Exact sciences and technology; Failure; Failure times; Heterogeneity; Machine components; Mathematical models; Mathematics; Mechanical engineering. Machine design; Operational research and scientific management; Operational research. Management science; Parametric inference; Power law; Power-law process; Powertrain systems; Probability and statistics; Probability distribution; Reliability theory. Replacement problems; Repairable systems; Sciences and techniques of general use; Shafts, couplings, clutches, brakes; Springs and dampers; Statistics; Power-law process; Heterogeneity; Repairable systems; Bayes inference; Powertrain systems; Probability distribution; Covariate; Modeling; Random effect; Working condition; System analysis; Power law; Repairable system; Bayes estimation; Bus; Mechanical drive; Rupture; Inference; Maintenance; Joint distribution; Real time; Failures; Real time system; Break up time; Urban transportation
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2014.04.009

This paper aims to model the failure pattern of repairable systems in presence of explained and unexplained heterogeneity. The failure pattern of each system is described by a Power Law Process. Part of the heterogeneity among the patterns is explained through the use of a covariate, and the residual unexplained heterogeneity (random effects) is modeled via a joint probability distribution on the PLP parameters. The proposed approach is applied to a real set of failure time data of powertrain systems mounted on 33 buses employed in urban and suburban routes. Moreover, the joint probability distribution on the PLP parameters estimated from the data is used as an informative prior to make Bayesian inference on the future failure process of a generic system belonging to the same population and employed in an urban or suburban route under randomly chosen working conditions. •We describe the failure process of buses powertrain system subject to heterogeneity.•Heterogeneity due to different service types is explained by a covariate.•Random effect is modeled through a joint pdf on failure process parameters.•The powertrain reliability under new future operating conditions is estimated.