Convolutional Neural Network of Mutual Information Specification and Its Application in Bearing Fault Feature Extraction

doi:10.3901/JME.2024.12.137

Abstract

Abstract: From the perspective of model fitting, we propose a convolutional neural networks model that regulates the relationship between model inputs and outputs through mutual information rules. The loss of the model is improved to enhance the feature extraction ability of the bearings and visualize the results. First, the vibration data is converted into an envelope spectrum as the input of the model so that the input layer has some physical meaning; then the multilayer convolution used for feature extraction is treated as a system and the output of the system is designed to be the same size as the input of the model so that the model is easy to understand; second, the attention mechanism layer is added to enhance the apparent features; finally, a loss function is designed to drive the convolutional system based on the mutual information law between the signal before and after noise reduction to extract more frequency components of the fault features. The simulated signals and real cases show that the visualization results of the proposed method are not only interpretable, but also effective in the extraction of fault features of bearings.

Key words: convolutional neural networks, fault feature extraction, mutual information, rolling bearing, feature visualization

CLC Number:

TG156

WANG Zhenya, LIU Tao, WU Xing. Convolutional Neural Network of Mutual Information Specification and Its Application in Bearing Fault Feature Extraction[J]. Journal of Mechanical Engineering, 2024, 60(12): 137-146.

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