Novel Time-Frequency Mode Decomposition and Information Fusion for Bearing Fault Diagnosis Under Varying-Speed Condition

Rolling bearing fault diagnosis is significant in rotating machinery daily maintenance. However, it is still difficult to diagnose the weak fault of rolling bearing under variable speed in some cases. In this article, a bearing fault diagnosis method under varying speed is given, which can extract the weak feature and diagnose weak fault effectively. First, a novel time-frequency mode decomposition (TFMD) method is proposed to decompose the signal into various modal components. Then, the feature fusion realizes the feature enhancement of each modal component. In addition, the cross correlation coefficient and signal-to-noise ratio are used as indexes in the comparison between TFMD and some other existing methods. A simulation analysis shows that the TFMD can avoid the modal aliasing and is more robust to speed error. Experimental verification shows that the proposed method has high accuracy in bearing fault diagnosis.

Automated summary

In this article, a method for diagnosing rolling bearing faults under varying speed is proposed, which can effectively extract weak fault features and diagnose them. The proposed method decomposes the signal into modal components using a novel time-frequency mode decomposition (TFMD) method and enhances the features of each modal component through feature fusion. Simulation and experimental results demonstrate the high accuracy and robustness of the TFMD in bearing fault diagnosis.