Fault diagnosis based on TOPSIS method with Manhattan distance

Wen Jiang,Xinyang Deng,Linfeng Gou,Meijuan Wang

doi:10.1177/1687814019833279

Wen Jiang, Xinyang Deng + Show 2 more

Open Access

https://doi.org/10.1177/1687814019833279

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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.

Highlights

With the development of modern large-scale production, the complexity of machinery equipment is increasing
A method based on TOPSIS with Manhattan distance for fault diagnosis is presented
Manhattan distance is used to calculate the similarity between the fault model and the detection sample

Summary

Introduction

With the development of modern large-scale production, the complexity of machinery equipment is increasing. Machinery equipment fault diagnosis technology is one of the basic measures to ensure the safe operation of equipment, which can take early warning of the development of equipment fault, make judgment on the causes of fault, propose countermeasures and suggestions, and avoid or reduce the occurrence of accidents. The fault diagnosis technology has received great attention, and it has become one of the highlight research directions in the self-control community, such as expert system, neural network, and fuzzy logic–based system.[1] Fault can be understood as at least one important characteristic or variable in the system deviates from the normal range. Fault can be considered as any abnormal phenomenon of the system, which causes the system to show undesirable characteristics.[2] In the process of fault diagnosis, due to the randomness and fuzziness of fault, the relationship between fault and its characteristic is complicated. One fault often can show various characteristics, and the same characteristic can be caused by different

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Conclusion