Manifold learning and Lempel-Ziv complexity-based fault severity recognition method for bearing

Abstract

The health condition of bearings was related to the operation status of mechanical equipment, and there were relatively few methods to recognize the severity of bearing faults. Although the Lempel-Ziv complexity (LZ complexity) has been proven to be effective in recognizing the bearing fault severity, the LZ was affected by noise. And the frequency band selection-based methods were used for noise reduction for LZ, which might cause the loss of frequency band information, and affect the accuracy of LZ. Aiming at this issue, this paper proposed a bearing fault severity recognition method based on manifold learning. Firstly, the original signal was reconstructed to high-dimensional phase space. Then, local tangent space alignment was used to extract effective signals aliased in noise. Finally, the LZ complexity was obtained according to the signal after noise reduction. The proposed method was verified by constructed data and the real experimental dataset of bearing, respectively. The results showed that the proposed method can be effectively used in the fault severity recognition of bearing.