Optimizing a condition-based maintenance policy by taking the preferences of a risk-averse decision maker into account

Reasonably accurate remaining-useful-life (RUL) predictions allow for the introduction of maintenance policies where resources, such as spare parts and personnel, are only acquired based on the predicted need. For some assets, such a policy will help reduce the cost of renewals but will also increase the probability of renewal cycles with long downtime and associated large losses. From a decision theoretical point of view decision makers are often risk-averse and therefore their financial risk tolerance should be considered. This paper presents a pro-cedure based on expected utility theory for the optimization challenge. To calculate the expected utility the characteristic function is used to find the full probability mass function of the maintenance cost in a finite time interval. A numerical example and a case study, based on data from an offshore oil and gas platform, are pre-sented to illustrate the proposed model. These examples show that using the long-run cost rate to optimize the presented maintenance policy may lead to decisions that are not in line with the preferences of a risk-averse decision maker.

Automated summary

Reasonably accurate RUL predictions can assist in formulating maintenance policies and acquiring resources based on predicted needs, but may also increase the risk of long downtime and substantial losses. Decision makers' financial risk tolerance should be taken into account when optimizing the challenge.