Intelligent fault diagnosis based on weighted symptom tree model and fault propagation trends

Abstract

This paper presents a fault detection and diagnosis methodology based on the weighted symptom tree model and pattern matching between the coming fault propagation trend and the simulated one. At the first step, backward chaining is used to find the possible cause candidates for the faults. The weighted symptom tree model(WSTM) is used to generate these candidates. The weights are determined by dynamic simulations. Using WSTM, the methodology can generate the cause candidates and rank them according to the probability. At the next step, the fault propagation trends identified from the partial or complete sequence of measurements are compared to the standard fault propagation trends which have been generated using dynamic simulation and stored a priori. A pattern matching algorithm based on a number of triangular episodes is used to effectively match those trends. The proposed methodology has been illustrated using two case studies and showed satisfactory diagnostic resolution.