Semi-Markov models of inspection-based maintenance with empirical data from case studies on hydrant pumps

Abstract

Considerable benefits have been gained from condition-based maintenance (CBM) utilizing continuous monitoring integrated with information technology. However, periodic inspection for CBM is still used widely as a practically helpful method to know the condition of the equipment. This paper starts from a case study where a maintenance log recorded by periodic inspection from five hydrant pumps is used to estimate the required parameter for maintenance modeling. To process the data for CBM, two schemes are taken into consideration: Inference of condition indicator through repair activities and reflection of non-observable events with virtual nodes. A CBM model of inspection-based preventive maintenance with discrete data is developed using the Markov model. The semi-Markov process is adopted then with more flexibility allowing the Weibull distributed sojourn times and the Multiphase Markov process is suggested to reflect the periodic inspection. Thus, the model for pumps takes into account both SMP and multiphase Markov process. Monte-Carlo simulations are generated to calculate state probability and the number of maintenances. An analytical solution is proposed by the transition probability of embedded Markov chain (EMC) and sojourn time of SMP. The developed CBM models are verified and compared based on analysis results and empirical data.