Fault Diagnosis Based On One-Dimensional Deep Convolution Neural Network

Aiming at the problems of low accuracy and poor generalization ability of bearing fault diagnosis based on machine learning (ML), this paper proposes the method of deep learning (DL) to solve the above problems. Combined with the bearing signal one-dimensional characteristics, this paper proposes a one-dimension deep convolutional neural network(1D-DCNN). First of all, the original bearing vibration signal is directly input the 1D-DCNN frame structure, then 1D-DCNN frame structure is used to automatic feature extraction. Next, we use softmax regression to classified fault samples and normal samples, the confusion matrix shows that the accuracy of the 1D-DCNN model for fault diagnosis. Finally, T-SNE algorithm is used to reduce the dimension of extracted features and visualize data features, which proves that the 1D-DCNN model has a good feature extraction ability. We use bearing data sets from Case Western Reserve University (CWRU) to verify the model of 1D-DCNN. According to the result, we can see that the 1D-DCNN frame structure not only effectively extracts and diagnoses the original signal, but also has high fault identification accuracy. It shows the advantages of the 1D-DCNN model in extracting data features and fault diagnosis. The model of 1D-DCNN is better than the mainstream fault diagnosis method of support vector machine (SVM) and probabilistic neural networks (PNN).