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Multi-source information deep fusion for rolling bearing fault diagnosis based on deep residual convolution neural network

Aiming at the difficulty of identifying weak fault of rolling element bearing (REB) accurately using only one single fault signal evidence domain, a multi-source information deep fusion diagnosis method for REB based on multi-synchrosqueezing transform (MSST) and deep residual convolution neural network (DRCNN) is presented in this paper, which combines the potential application of MSST in fault feature extraction of REB and the advantages. Firstly, the signals of REB under different running states are transformed by short-time Fourier transform (STFT) and MSST, respectively, to obtain the STFT and MSST time-frequency spectrum symptom set. Then, multiple DRCNNs are built to perform feature learning on the obtained time-frequency multi-symptom domain information, thereby a mapping between the local feature space and the fault space is established. Finally, the testing feature vectors obtained same as the processes of training feature vectors are input into the trained DRCNN models for automatic recognition. The validity of the proposed method is verified by experiment, and the overall average recognition success rate of the proposed method reaches above 95%. Besides, its advantage is also compared with the other related methods.