Fault Diagnosis Method of Planetary Gearbox based on Wavelet Time-frequency Diagram and Convolutional Neural Network - Jianhua Zhou; Pan Zheng; Shuaixing Wang; Shijing Wu; Xiaosun Wang (Jixie chuandong)
Difficulties are always encountered when distinguish the fault types in the planetary gearbox diagnosis. A new fault diagnosis method with implementation of wavelet time-frequency diagram and convolutional neural network is proposed. At first，the continuous wavelet transform is used on the original signal to obtain the wavelet time-frequency diagrams. Secondly，the wavelet time-frequency diagrams are processed and compressed，the processed wavelet time-frequency diagrams are input into the convolutional neural network to classify and identify. Finally，the wavelet basis function and convolution neural network parameters are adjusted in order to get an ideal diagnosis model. Experimental results show that the proposed method has better diagnostic accuracy and robustness than the BP neural network when the speed of training set data and test set data is different. This approach provides a reference for planetary gearbox fault diagnosis.