| Summary: | Rolling-element bearing fault diagnosis has some problems in the applied environment, such as low signal-to-noise ratio, weak feature extraction, low efficiency of feature learning and the complex structure of diagnosis models. A fault diagnosis method based on the comprehensive index method, complete ensemble empirical mode decomposition with adaptive noise independent component analysis (CEEMDANICA) and two-dimensional convolutional neural network (TDCNN) is proposed. Firstly, the original vibration signal of the bearing is preprocessed by CEEMDANICA, and the ICA components with different frequencies are obtained. Secondly, the ICA components are selected as the sample set by using multiscale permutation entropy, correlation coefficient, kurtosis and box dimension. Finally, the sample set are trained and tested by a DCNN model to realize the fault diagnosis of different bearing fault types. In order to verify the reliability of the method, a bearing fault vibration monitoring platform for an electric vehicle two-speed automatic transmission was built to collect the bearing vibration signals of multiple fault types under different working conditions. The diagnostic accuracy of several deep learning models is compared. The results show that the proposed method can realize the single and compound fault diagnosis of rolling-element bearings in an automatic transmission, with a high degree of accuracy.
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