Fault diagnostic strategy of multivalued attribute system based on growing algorithm

Abstract

Traditionally, fault diagnostic strategy is used to obtain the optimal test sequence for binary systems. Actually, a lot of systems are not binary systems, such as multivalued attribute systems. Traditional algorithms generating the test sequence for binary systems and multivalued attribute systems select tests, and then identify and isolate the failure states based on the outcomes of tests. In this study, a novel diagnostic strategy for multivalued attribute system is introduced. This strategy chooses failure states and then finds a suitable test set for the selected failure states. This can avoid the backtracking approach of traditional algorithms. In order to implement this strategy, three main procedures are presented: (1) test sequencing problem is simplified to a combination of the basic test sets with unnecessary tests, and the sets for fault detection and isolation are defined, (2) the optimal test sequence generating algorithm for an individual failure state is proposed, and (3) the priority levels of failure state are determined based on the probability, and a new algorithm, which is used to generate the test sequence for all failure states, is presented. As the implementation process for the new algorithm resembles the growth of branches on a tree, it is defined as growing algorithm. Finally, two cases are used to show how the growing algorithm works, and stochastic simulation experiments are employed to validate universality and stability of the algorithm. The case studies and stochastic simulation experiments demonstrate that the results obtained by the growing algorithm are as accurate as those obtained by the rollout algorithm, and the growing algorithm needs a short running time. Therefore, the growing algorithm is suitable for multivalued attribute system, and it obtains good calculation results with a short running time and high efficiency.