Optimisation of Maintenance Policies Based on Right-Censored Failure Data Using a Semi-Markovian Approach

Abstract

This paper exposes the existing problems for optimal industrial preventive maintenance intervals when decisions are made with right-censored data obtained from a network of sensors or other sources. A methodology based on the use of the z transform and a semi-Markovian approach is presented to solve these problems and obtain a much more consistent mathematical solution. This methodology is applied to a real case study of the maintenance of large marine engines of vessels dedicated to coastal surveillance in Spain to illustrate its usefulness. It is shown that the use of right-censored failure data significantly decreases the value of the optimal preventive interval calculated by the model. In addition, that optimal preventive interval increases as we consider older failure data. In sum, applying the proposed methodology, the maintenance manager can modify the preventive maintenance interval, obtaining a noticeable economic improvement. The results obtained are relevant, regardless of the number of data considered, provided that data are available with a duration of at least 75% of the value of the preventive interval.

Relaxed Observer-Based H∞-Control for Markov Jump Fuzzy Systems with Incomplete Transition Probabilities and Sensor Failures

“…Markov jump systems (MJSs), a particular class of stochastic hybrid systems, have revealed versatile abilities to describe random quick changes, including sensor failures and abrupt changes of interconnected systems [1,2]. The past decade has witnessed various widespread experiments associated with discrete-time MJSs such as power systems [3] and communication systems [4].…”