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This paper considers the sequential fault diagnosis problem with unreliable tests which exist widely in practice. This problem involves real-time inference of the most likely set of failure sources, i.e., fault state, based on unreliable test outcomes. The purpose of this paper is to optimize test set and diagnostic strategy so as to cut down the test cost while isolating the fault accurately, and a method for optimal diagnosis strategy based on partially observable Markov decision process (POMDP) is presented. The components of the POMDP tailed to optimizing the diagnostic strategy are specified, and the solution to the POMDP-based model, namely the optimal strategy, is obtained to describe the optimal test sequence for fault diagnosis. The performance of the proposed method is evaluated with simulation experiments. All the results indicate that this method performs good in diagnostic efficiency and accuracy, even compared with the strategies of traditional methods.

The selection of tests required to make complex systems testable is a fundamental of system-level fault diagnosis. To evaluate the test selection, testability metric estimation (TME) is required. The influence of unreliable (imperfect) tests, whose outcomes are non-deterministic due to unstable environmental conditions, test equipment errors, and component tolerances, should be considered for accurate TME. Previously, researchers considered a TME model using a Bernoulli distribution with the assumption that the variations of different test outcomes are independent. However, this assumption is not always true. To address the issue, a joint distribution-based TME model was developed derived from the copula function to quantify the influence of dependent outcomes of unreliable tests. The efficacy of the developed TME model was verified with a linear voltage divider and a negative feedback circuit.

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. [ABSTRACT FROM AUTHOR]