Application of CNN network fused with GRU units in fault diagnosis of petroleum rotating machinery
A CNN network algorithm model that integrates GRU unit optimization improvement is proposed to address the problems of existing fault diagnosis algorithms for petroleum rotating machinery.Firstly,the wavelet packet algorithm is used to retain the characteristics of weak fault signals in the high frequency in-terval,and the ReLU function is selected as the activation function of the convolution layer of the CNN net-work to improve the running speed of the algorithm and suppress the excessive attenuation of the gradient value of the model.By integrating input gates and forgetting gates based on GRU units,the gradient disper-sion problem in CNN networks is suppressed,and the fault data training performance and classification de-tection performance of CNN network models are improved.The experiment results show that the CNN net-work that integrates GRU units has better fault diagnosis accuracy and classification ability than the existing fault diagnosis algorithms in both the training and testing sets,and the predicted values of the MAE function are closer to the true values.
gate recurrent unitconvolutional neural networkrotary table bearingdropout networkMAE function
万方数据
维普
