Unsupervised cross-domain rolling bearing fault diagnosis based on time-frequency information fusion

In recent years, data-driven methods have been widely used in rolling bearing fault diagnosis with great success, which mainly relies on the same data distribution and massive labeled data. However, bearing equipment is in normal working state for most of the time and operates under variable operating conditions. This makes it difficult to obtain bearing data labels, and the distribution of the collected samples varies widely. To address these problems, an unsupervised cross-domain fault diagnosis method based on time-frequency information fusion is proposed in this paper. Firstly, wavelet packet decompo-sition and reconstruction are carried out on the bearing vibration signal, and the energy eigenvectors of each sub-band are extracted to obtain a 2-D time-frequency map of fault features. Secondly, an unsu-pervised cross-domain fault diagnosis model is constructed, the improved maximum mean discrepancy algorithm is used as the measurement standard, and the joint distribution distance is calculated with the help of pseudo-labels to reduce data distribution differences. Finally, the model is applied to the motor bearing for comparison and verification. The results demonstrate its high diagnosis accuracy and strong robustness.(c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes an unsupervised cross-domain fault diagnosis method based on time-frequency information fusion to address the challenges of label scarcity and data distribution differences in bearing fault diagnosis. The method utilizes wavelet packet decomposition and reconstruction to extract fault features in the form of a 2-D time-frequency map, constructs an unsupervised cross-domain fault diagnosis model, and calculates the joint distribution distance using the improved maximum mean discrepancy algorithm and pseudo-labels. Experimental results on motor bearings demonstrate the high diagnosis accuracy and strong robustness of the proposed method.