A Rolling Bearing Fault Diagnosis Method via 2D Feature Map of CSCoh After Denoising and MSCNN Under Different Conditions

Abstract

The vibration signal of rolling bearing is interfered by the coupling of other various components and environment, which brings challenges to effective feature expression and the establishment of accurate predictor. A fault diagnosis method via 2D feature map of cyclic spectral coherence (CSCoh) after denoising and multi-scale convolutional neural network (MSCNN) under different conditions is proposed. The original vibration signal is processed by DTCWPT and threshold denoising, and a 2D feature map extraction model based on CSCoh is established to reflect hidden periodic characteristics. The feature differences can be highlighted and the coupling interference can be further eliminated. The multi-scale convolution kernels are proposed to build a parallel structure, and a fusion structure is followed. Benefit by the structures in MSCNN, the local and global features of 2D feature map can be preserved comprehensively, the learning ability and diagnostic performance can be improved. Finally, the verification experiments of rolling bearings in different positions under different operating conditions are carried out, and the experimental results show the proposed method has better generalization ability and comprehensive accuracy of more than 95% under different conditions.