A small sample bearing fault diagnosis method based on variational mode decomposition, autocorrelation function, and convolutional neural network

Bearing fault is a factor that directly affects the reliability of the machine tools. Small sample bearing fault diagnosis plays an important role to improve the reliability of machine tools. However, the over-fitting and weak performance are common problems of small sample bearing fault diagnoses based on deep learning. This paper proposed a different method based on data enhancement and convolutional neural networks (CNN). The method firstly decomposes the vibration signals of the rolling bearing according to the optimal decomposition criterion of variational mode decomposition (VMD). Then, it selects the modes according to the fault frequency characteristics and filters the selected modes into multiple sub-band signals by band-pass filters. Moreover, it computes out the autocorrelation peak vector of the sub-band signals. Finally, the method uses the fault diagnosis network made from a 4-layer neural network, automatically extracts bearing fault features, and predicts the fault types of the testing signals. The experiment shows that the proposed method has a 99% accuracy rate in the rolling bearing fault data set XJTU-SY and requires fewer training samples than the latest methods of NKH-KELM and VMD-CNN. The proposed method has high accuracy under the small sample conditions, which makes it applicable in some practical CNC machine tools with difficulties obtaining bearing samples.