Fault Diagnosis of Rolling Bearings Based on the Improved Symmetrized Dot Pattern Enhanced Convolutional Neural Networks

Purpose: The main purpose of this paper is to change the structure of the SDP to include more fault information. Furthermore, improve the diagnostic accuracy and anti-noise performance of the bearing fault diagnosis method based on SDP. Methods: First, a multi-interval asymmetric dot pattern (MADP) is proposed by modifying the expression of SDP. Then, the improved SDP (multi-modal multi-interval asymmetric dot pattern, MMADP) is established by the MADP method fused with the multiple effective IMF components which are obtained through CEEMDAN decomposition. Finally, a bearing fault diagnosis model is established based on MMADP and convolutional neural network. Results: The effectiveness of the proposed fault diagnosis method is validated on the CWRU dataset. The results indicate that under Gaussian white noise with a signal-to-noise ratio (SNR) of above 4 dB and − 6 dB, the accuracy of the proposed fault diagnosis method reaches 100 and 93.3%, respectively. Conclusion: In this paper, a method (MADP) transforming time series signals into images is proposed, and a method for fault diagnosis of rolling bearings is formed through combination of CEEMDAN and CNN. The bearing fault diagnosis method has good anti-noise performance, and the MADP has potential value in the processing of sound signals.