Fault diagnosis based on TOPSIS method with Manhattan distance

Abstract

Fault diagnosis is important for the maintenance of machinery equipment. Due to the randomness and fuzziness of fault, the relationship between fault types and their characteristics are complicated. Therefore, the determination of fault type is a challenging part of machinery fault diagnosis with the traditional method. To tackle this problem, a fault diagnosis approach based on the technique for order performance by similarity to ideal solution with Manhattan distance is presented in this article. First, the similarity measure between the fault model and the detection sample is constructed based on the Manhattan distance. Then, the similarity is transformed into intuitionistic fuzzy set and the generated intuitionistic fuzzy set is fused by the intuitionistic fuzzy weighted averaging operator. On this basis, the technique for order performance by similarity to the ideal solution approach is utilized to obtain the final rank to ascertain the fault type. The proposed method can handle an intricate relationship between multiple fault types and their various fault characteristics and better express uncertain information. Finally, a fault diagnosis example of the machine rotor and comparative study are conducted to illustrate the application and the effectiveness of the proposed method.