Fault Diagnosis for Bearings of Unbalanced Data Based on Feature Generation

doi:10.16182/j.issn1004731x.joss.20-FZ0458

Abstract

Abstract: Focus on the sample imbalance and insufficiency caused by the difficulty to obtain a sufficient number of fault samples in actual production.A model for rolling bearings by combining Convolutional Neural Networks and Synthetic Oversampling is presented.The frequency domain signals is used as the input of the model,and the features are extracted by the Convolutional Neural Network.The new features are generated by Synthetic Oversampling and the data equalization is realized.The model completes the classification by putting all of the features into the Support Vector Machine,and the fault diagnosis of the rolling bearings is carried out.The comparison experiments results show that the method can effectively solve the problem of data imbalance.

Key words: rolling bearing, fault diagnosis, feature generation, convolutional neural network

CLC Number:

TP391.9

Fan Minglu, Wang Yan, Ji Zhicheng. Fault Diagnosis for Bearings of Unbalanced Data Based on Feature Generation[J]. Journal of System Simulation, 2020, 32(12): 2438-2448.

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