A new transfer learning fault diagnosis method using TSC and JGSA under variable condition

Abstract

It is very difficult to obtain the label data of rolling bearings under the complicated and variable working conditions, which results in low diagnosis accuracy. Transfer sparse coding(TSC) is a new feature representation method, which can effectively extract features from data matrix. Joint geometric and statistical alignment (JGSA) is a domain adaptation method, which can reduce the distribution shift and geometric shift between domains. In order to make full use of the feature extraction ability of the TSC and the transfer classification ability of the JGSA, a new transfer learing fault diagnosis(TSC-JGSA) method based on combining the characteristics of the TSC and JGSA is proposed to realize the fault diagnosis of rolling bearings under variable working conditions in this paper. In the TSC-JGSA, the fast Fourier transform technology is used to transform the time-domain signals into frequency-domain amplitudes. Then the TSC is used to effectively extract the deep features from the obtained frequency-domain amplitudes in order to construct a sparse feature matrix, which is input into the JGSA in order to realize the fault diagnosis of rolling bearings. Finally, the vibration data of rolling bearings under variable working conditions is used to prove the effectiveness of the TSC-JGSA. The experiment results show that the TSC-JGSA can effecrively solve the problem of lacking label data in actual engineering by using label data in the laboratory, and obtan higher diagnosis accuracy than other compared methods. It provides a new diagnosis idea for rotating machinery.