[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(
)
基于随机森林和自编码的滚动轴承多视角特征融合
)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
- 基于随机森林和自编码的滚动轴承多视角特征融合
- 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
- 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.
- 为了提高复杂工况下滚动轴承故障诊断的准确性和鲁棒性, 提出了基于振动信号多视角特征深度融合的故障诊断方法.首先, 通过傅里叶变换、希尔伯特变换以及经验模态分解(EMD)提取滚动轴承振动信号的时域、频域及时频域特征;然后, 利用随机森林模型(RF)选择多视角特征集中与轴承运行状态相关性较大的特征, 将选出特征通过自编码器(AE)进行深度融合以进一步降低特征的冗余性;最后, 将融合后的多视角特征作为支持向量机(SVM)的输入, 以对其有效性进行评价.实验结果表明, 提出的多视角特征融合的故障诊断方法能有效地反映轴承运行状态之间的差异, 提高了滚动轴承故障诊断的准确率.
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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.