Research on Fault Diagnosis Algorithm Based on Multiscale Convolutional Neural Network

Abstract

The traditional method of fault diagnosis is essentially looking for the optimal combination of feature extractor and classifier. In this process, it is necessary to manually extract the expert knowledge of features and related fields, which greatly limits the versatility and generalization of the algorithm. The convolutional neural network has the characteristics of “end-to-end”, which can directly perform the whole process of feature extraction, feature dimension reduction and classifier classification on the original signal through a neural network. The traditional convolutional neural network model has the same convolution kernel shape per convolutional layer, and the feature extraction ability is relatively limited. However, the fault signals of equipment or components are often complex and variable, and data features are difficult to mine. In view of the above problems, this paper proposes a fault diagnosis method based on multi-scale convolutional neural network. Based on the traditional convolutional neural network, the diversity of convolutional layer convolution kernels is increased. Finally, the feature data extracted by each scale convolution kernel is merged. The experiment proposed in this paper has a high fault recognition rate by experimenting with the bearing fault public data set.