MCMC-based Efficient Maintenance Plan Decision

Abstract

In recent years, it has been an essential policy to monitor real-time health states of facilities and determine when to perform maintenance in order to ensure the high operation atio and improve work efficiency. In this paper, target facilities diagnose their own health states by analyzing timeseries sensor data and transmit warning data and failure data to the monitoring center. These data include date and time of occurrence and warning/failure code which identifies the factor. Utilizing these data, we propose an MCMC-based maintenance plan decision to reduce the failures and the workloads. Firstly, state-based warning patterns which are composed of several warning codes are extracted. At that time, to avoid the state explosion, only warning patterns which are closely related to failure occurrence are extracted based on the time interval from warning states to failure state. Secondly, warning patterns are modeled based on N-th order Markov model. Finally, maintenance plan is decided based on failure probability. Experiment to evaluate whether the facilities can be maintained before failure occurs proved that this approach could actually reduce the number of failures and the frequency of dispatches of maintenance workers.