[1] Sun Wenqing, Deng Aidong, Deng Minqiang, Zhu Jing, et al. Multi-view feature fusion for rolling bearing faultdiagnosis using random forest and autoencoder [J]. Journal of Southeast University (English Edition), 2019, 35 (3): 302-309. [doi:10.3969/j.issn.1003-7985.2019.03.005]

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Multi-view feature fusion for rolling bearing faultdiagnosis using random forest and autoencoder()

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

35

Issue:

2019 3

Page:

302-309

Research Field:

Mechanical Engineering

Publishing date:

2019-09-30

Info

Title:

Multi-view feature fusion for rolling bearing faultdiagnosis using random forest and autoencoder

Author(s):

Sun Wenqing;  Deng Aidong;  Deng Minqiang;  Zhu Jing;  Zhai Yimeng;  Cheng Qiang;  Liu Yang

National Engineering Research Center of Turbo-Generator Vibration, Southeast University, Nanjing 210096, China
School of Energy and Environment, Southeast University, Nanjing 210096, China

Keywords:

multi-view features;  feature fusion;  fault diagnosis;  rolling bearing;  machine learning

PACS:

TH133.33

DOI:

10.3969/j.issn.1003-7985.2019.03.005

Abstract:

To improve the accuracy and robustness of rolling bearing fault diagnosis under complex conditions, a novel method based on multi-view feature fusion is proposed. Firstly, multi-view features from perspectives of the time domain, frequency domain and time-frequency domain are extracted through the Fourier transform, Hilbert transform and empirical mode decomposition(EMD).Then, the random forest model(RF)is applied to select features which are highly correlated with the bearing operating state. Subsequently, the selected features are fused via the autoencoder(AE)to further reduce the redundancy. Finally, the effectiveness of the fused features is evaluated by the support vector machine(SVM). The experimental results indicate that the proposed method based on the multi-view feature fusion can effectively reflect the difference in the state of the rolling bearing, and improve the accuracy of fault diagnosis.

References:

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Memo

Memo:

Biographies: Sun Wenqing(1994—), male, graduate; Deng Aidong(corresponding author), male, doctor, professor, dnh@seu.edu.cn.
Foundation item: The National Natural Science Foundation of China(No. 51875100).
Citation: Sun Wenqing, Deng Aidong, Deng Minqiang, et al.Multi-view feature fusion for rolling bearing fault diagnosis using random forest and autoencoder[J].Journal of Southeast University(English Edition), 2019, 35(3):302-309.DOI:10.3969/j.issn.1003-7985.2019.03.005.

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