Fault Diagnosis for Bearings of Unbalanced Data Based on Feature Generation

Authors

Minglu Fan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Jiangnan University,Wuxi 214122,China;
Wang Yan, Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Jiangnan University,Wuxi 214122,China;
Zhicheng Ji, Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Jiangnan University,Wuxi 214122,China;

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.

Recommended Citation

Fan, Minglu; Yan, Wang; and Ji, Zhicheng (2020) "Fault Diagnosis for Bearings of Unbalanced Data Based on Feature Generation," Journal of System Simulation: Vol. 32: Iss. 12, Article 16.
DOI: 10.16182/j.issn1004731x.joss.20-FZ0458
Available at: https://dc-china-simulation.researchcommons.org/journal/vol32/iss12/16

First Page

2438

Revised Date

2020-07-08

DOI Link

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

Last Page

2448

CLC

TP391.9

Recommended Citation

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.

DOI

10.16182/j.issn1004731x.joss.20-FZ0458