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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.

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This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies.

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