Novel algorithms for sequential fault diagnosis based on greedy method

Heng Tian, Fuhai Duan, Yong Sang and Liang Fan

Journal of Risk and Reliability, 2020, vol. 234, issue 6, 779-792

Abstract: Test sequencing for binary systems is a nondeterministic polynomial-complete problem, where greedy algorithms have been proposed to find the solution. The traditional greedy algorithms only extract a single kind of information from the D -matrix to search the optimal test sequence, so their application scope is limited. In this study, two novel greedy algorithms that combine the weight index for fault detection with the information entropy are introduced for this problem, which are defined as the Mix 1 algorithm and the Mix 2 algorithm. First, the application scope for the traditional greedy algorithms is demonstrated in detail by stochastic simulation experiments. Second, two new heuristic formulas are presented, and their scale factors are determined. Third, an example is used to show how the two new algorithms work, and four real-world D- matrices are employed to validate their universality and stability. Finally, the application scope of the Mix 1 and Mix 2 algorithms is determined based on stochastic simulation experiments, and the two greedy algorithms are also used to improve a multistep look-ahead heuristic algorithm. The Mix 1 and Mix 2 algorithms can obtain good results in a reasonable time and have a wide application scope, which also can be used to improve the multistep look-ahead heuristic algorithm.

Keywords: D-matrix; greedy algorithm; information entropy; sequential fault diagnosis; test sequence (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:risrel:v:234:y:2020:i:6:p:779-792

DOI: 10.1177/1748006X20914498

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