Selective maintenance is regarded as a type of profit-generating maintenance policy, playing an important role in balancing limited maintenance resources with system performance. Since 1988, increasing interest has been focused on this research area. Nevertheless, to the best of our knowledge, there is a lack of critical reviews of selective maintenance. This paper is the first systematic review focusing on this relevant topic. In this work, a definition and some specific features of selective maintenance are elaborated. Based on these features, a set of criteria that have been considered in selective maintenance optimization are summarized into 3 categories: system characteristics, maintenance characteristics, and mission profile characteristics. Based on these criteria, a comprehensive literature review on selective maintenance is undertaken. The solution approaches, as well as a general procedure for selective maintenance optimization, are discussed. Finally, some possible directions for further research are provided.

This paper aims to construct a finite impulse response (FIR) based fault estimator for a class of linear discrete time-varying systems (LDTV) with multiplicative noise. Drawing support of intensive stochastic analyses and matrix manipulations, a novel performance index is proposed such that the fault estimation error is minimized in stochastic sense. A necessary and sufficient condition is established to guarantee the existence of the FIR-based fault estimator with satisfied estimation accuracy. The optimal gain of the desired fault estimator is calculated in an analytical way by minimizing the aforementioned performance index. Several examples are presented to demonstrate the effectiveness and superiority of the proposed methods.

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