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

PurposeThe 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.MethodsFirst, 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.ResultsThe 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.ConclusionIn 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.