A novel method for optimal test sequencing under unreliable test based on Markov Decision Process

In this paper, a novel method for optimal solution in long-run is proposed to solve the test sequencing problem under unreliable tests that exist widely in real applications. The fault diagnosis process is presented and reformulated as a typical Markov Decision Process model, with the uncertainty of the tests is descripted by false alarm and detection probabilities which are to be the transition probabilities of the model. Moreover, the repeated test is adopted to improve the reliability of fault diagnosis. The cost and information gain are both considered in test chosen to achieve fast diagnosis with minimum cost. An application on the launcher device of a missile is studied in detail, as well as the comparisons in diagnostic performance among the proposed solution and the ones of traditional methods. Both the simulation and results illustrate the validity and feasibility of the proposed method.