Open AccessJoint feature enhancement mapping and reservoir computing for improving fault diagnosis performance
Author(s) -
Lingzhen Kong,
Yuancheng Huang,
Qibing Yu,
Jianyu Long,
Shuai Yang
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1207/1/012020
Subject(s) - robustness (evolution) , feature (linguistics) , computer science , artificial intelligence , robot , pattern recognition (psychology) , joint (building) , data mining , engineering , structural engineering , philosophy , biochemistry , chemistry , linguistics , gene
Complicated industrial robot structure and harsh working conditions may cause signal features collected in the condition monitoring process to be seriously disturbed. In this paper, a joint feature enhancement mapping and reservoir computing (FEM-RC) method is presented to handle the industrial robot fault diagnosis problem. Firstly, a feature enhancement mapping (FEM) method is proposed to achieve intraclass distance minimization and interclass distance equalization to obtain an enhanced feature matrix. Then, the first reservoir computing (RC) network is adopted to map the original feature matrix to the feature enhancement matrix, and the second RC network is for fault type classification. The results of the experiment carried out on a six-axial industrial robot demonstrate that compared with other peer models, the present FEM-RC has better fault diagnosis performance and robustness.