Abstract:  Under the condition of strong background noise, the fault characteristics of the power end bearing of the fracturing vehicle were weak, resulting in a low accuracy of bearing fault diagnosis. To solve this problem, a fault identification method for the power end bearing of the fracturing vehicle based on enhanced Laplace wavelet (ELW) and enhanced convolutional neural network(ECNN)was proposed. Firstly, a selection strategy of Laplace wavelet oscillation frequency parameter was proposed, so that the best frequency parameters were searched by the Laplace wavelet. Secondly, the collected vibration signals of the power end of the fracturing truck were denoised by enhanced Laplace wavelet, and the self-attention mechanism and encoder structure and decoder structure were introduced on the basis of the onedimensional convolutional neural network. Then, the enhanced convolutional neural network model was constructed. Finally, the de-noised signals of the power end of the fracturing vehicle were fed into enhanced convolutional neural network for automatic feature extraction and fault identification. In order to verify the effectiveness and advancement of the method, it was compared with other methods (models). The research results show that, the combined model of the enhanced Laplace wavelet(ELW) and enhanced convolutional neural network(ECNN) has an accuracy rate of 99.67% in fault identification of the power end of the fracturing vehicle, and the testing time of individual sample is 0.14 s. The combined model of the enhanced Laplace wavelet and enhanced convolutional neural network has superior fault recognition performance in terms of recognition accuracy, recall, F1 score and statistical test.
Key words: fracturing vehicle; condition of strong background noise; automatic feature extraction; fault identification; enhanced Laplace wavelet(ELW); enhanced convolutional neural network (ECNN)