Gear fault diagnosis based on SGMD noise reduction and CNN

Abstract

Gear vibration fault signals are non-stationary and nonlinear, so it is very difficult to accurately extract the fault characteristics for diagnosis. As symplectic geometry mode decomposition (SGMD) has shown excellent decomposition performance and noise robustness in signal processing. A novel gear fault diagnosis method, that is, SGMD-CNN, is proposed combined SGMD with a convolutional neural network (CNN). The noise of the gear vibration fault signal is reduced through the use of SGMD method, and several symplectic geometry components (SGC) are screened out according to the kurtosis value respectively. The reconstructed signal based on the selected SGCs is used as the input of the convolutional neural network. The ability of the convolutional neural network to learn features and classification is used to realize intelligent fault diagnosis of gear. The proposed SGMD-CNN is verified by the gear fault data. Based on the same convolution neural network model, the accuracy of the established SGMD-CNN model is 100%. It is about 32% higher than the accuracy of the simple CNN model and about 16% higher than the accuracy of the EMD-CNN model. The results show that the method is effective in classifying and identifying different types of gear failures.