The Application of SVM Based on Auto-encoder in Bearing Fault Diagnosis
[1]Lei Wenping,Wu Xiaolong,Chen Chaoyu,et al.The Application of SVM Based on Auto-encoder in Bearing Fault Diagnosis[J].Journal of Zhengzhou University (Engineering Science),2018,39(05):68-72.[doi:10.13705/j.issn.1671-6833.2018.05.013]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39
Number of periods:
2018 05
Page number:
68-72
Column:
Public date:
2018-08-21
- Title:
- The Application of SVM Based on Auto-encoder in Bearing Fault Diagnosis
- Keywords:
- Support Vector Machine; Autoencoder Unsupervised Feature Extraction; Empirical Mode Decomposition; Information Entropy; Fault Diagnosis
- CLC:
- -
- Abstract:
- The fault feature should be extracted before the SVM was applied to the bearing fault diagnosis. In the previous feature signal extraction, it was often based on the existing knowledge model. With the application and promotion of DNN in recent years, AE had a special advantage in feature extraction. As an unsupervised learning method, AE could extract the features of the signal based on data driven, making the feature extraction no longer depends on prior knowledge, and the whole fault diagnosis processed more intelligent. In this paper, the improved AE、DAE，were used to extract the features of the bearing signals, and the fault diagnosis was carried out by SVM. Finally, by compared with the SVM based on EMD energy entropy feature extraction, the superiority of DAE-SVM with unsupervised learning method was reflected in beraing fault diagnosis, and its diagnostic accuracy was neraly 100%
- References:
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Last Update:
2018-08-22