Joint Distribution-Based Test Selection for Fault Detection and Isolation Under Multiple Faults Condition

Abstract

Effective and comprehensive fault information can reduce the maintenance cost of a system. The selection of a proper test is important to obtain accurate information for fault detection and isolation (FDI) once a fault occurs. In real systems, the FDI task is made difficult by the correlations in the physical behaviors of the components and the possible coexistence of multiple faults. In this article, a new type of test selection model based on deep joint distribution is proposed, which takes into account the dependence and ambiguity groups. The optimal test can, then, be selected by an improved discrete binary particle swarm optimization (IBPSO) algorithm. The strengths of the proposed optimal test selection strategy are: 1) it can greatly improve the accuracy of test selection; 2) it considers the influence of multiple faults; 3) the dependence problem of the test outcomes can be addressed; and 4) it is computationally efficient and capable of selecting optimal test points meeting the requirements of real-time FDI. The application to a negative feedback circuit demonstrates the performance of the proposed method.