On reliability challenges of repairable systems using hierarchical bayesian inference and maximum likelihood estimation

•Applying Non-Homogenous Poisson Process (NHPP) and Homogenous Poisson Process (HPP) to predict the condition of a system.•Presenting the possible differentiation within failure assessment of random processes.•Investigating the effect of utilizing observed data in inter-arrival failure time modelling.•The model was examined on degradation process of the Natural Gas Regulating and Metering Stations (NGRMS). Failure modelling and reliability assessment of repairable systems has been receiving a great deal of attention due to its pivotal role in risk and safety management of process industries. Meanwhile, the level of uncertainty that comes with characterizing the parameters of reliability models require a sound parameter estimator tool. For the purpose of comparison and cross-verification, this paper aims at identifying the most efficient and minimal variance parameter estimator. Hierarchical Bayesian modelling (HBM) and Maximum Likelihood Estimation (MLE) approaches are applied to investigate the effect of utilizing observed data on inter-arrival failure time modelling. A case study of Natural Gas Regulating and Metering Stations in Italy has been considered to illustrate the application of proposed framework. The results highlight that relaxing the renewal process assumption and taking the time dependency of the observed data into account will result in more precise failure models. The outcomes of this study can help asset managers to find the optimum approach to reliability assessment of repairable systems.