Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor
In the field of rotor fault pattern recognition, most of classical pattern recognition methods generally operate in feature vector spaces where different feature values are stacked into one-dimensional (1D) vector and then processed by the classifiers. In this paper, time-frequency image of rotor vi...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2017-01-01
|
| Series: | International Journal of Rotating Machinery |
| Online Access: | http://dx.doi.org/10.1155/2017/6542348 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832566218070949888 |
|---|---|
| author | Hui Li Xiaofeng Liu Lin Bo |
| author_facet | Hui Li Xiaofeng Liu Lin Bo |
| author_sort | Hui Li |
| collection | DOAJ |
| description | In the field of rotor fault pattern recognition, most of classical pattern recognition methods generally operate in feature vector spaces where different feature values are stacked into one-dimensional (1D) vector and then processed by the classifiers. In this paper, time-frequency image of rotor vibration signal is represented as a texture feature tensor for the pattern recognition of rotor fault states with the linear support higher-tensor machine (SHTM). Firstly, the adaptive optimal-kernel time-frequency spectrogram visualizes the unique characteristics of rotor fault vibration signal; thus the rotor fault identification is converted into the corresponding time-frequency image (TFI) pattern recognition. Secondly, in order to highlight and preserve the TFI local features, the TFI is divided into some TFI subzones for extracting the hierarchical texture features. Afterwards, to avoid the information loss and distortion caused by stacking multidimensional features into vector, the multidimensional features from the subzones are transformed into a feature tensor which preserves the inherent structure characteristic of TFI. Finally, the feature tensor is input into the SHTM for rotor fault pattern recognition and the corresponding recognition performance is evaluated. The experimental results showed that the method of classifying time-frequency texture feature tensor can achieve higher recognition rate and better robustness compared to the conventional vector-based classifiers, especially in the case of small sample size. |
| format | Article |
| id | doaj-art-82a18d2db94645289177ada4d95c2ac2 |
| institution | Kabale University |
| issn | 1023-621X 1542-3034 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Rotating Machinery |
| spelling | doaj-art-82a18d2db94645289177ada4d95c2ac22025-02-03T01:04:53ZengWileyInternational Journal of Rotating Machinery1023-621X1542-30342017-01-01201710.1155/2017/65423486542348Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature TensorHui Li0Xiaofeng Liu1Lin Bo2Chongqing College of Electronic Engineering, Chongqing 400444, ChinaThe State Key Laboratory of Mechanical Transmission, Chongqing 400044, ChinaThe State Key Laboratory of Mechanical Transmission, Chongqing 400044, ChinaIn the field of rotor fault pattern recognition, most of classical pattern recognition methods generally operate in feature vector spaces where different feature values are stacked into one-dimensional (1D) vector and then processed by the classifiers. In this paper, time-frequency image of rotor vibration signal is represented as a texture feature tensor for the pattern recognition of rotor fault states with the linear support higher-tensor machine (SHTM). Firstly, the adaptive optimal-kernel time-frequency spectrogram visualizes the unique characteristics of rotor fault vibration signal; thus the rotor fault identification is converted into the corresponding time-frequency image (TFI) pattern recognition. Secondly, in order to highlight and preserve the TFI local features, the TFI is divided into some TFI subzones for extracting the hierarchical texture features. Afterwards, to avoid the information loss and distortion caused by stacking multidimensional features into vector, the multidimensional features from the subzones are transformed into a feature tensor which preserves the inherent structure characteristic of TFI. Finally, the feature tensor is input into the SHTM for rotor fault pattern recognition and the corresponding recognition performance is evaluated. The experimental results showed that the method of classifying time-frequency texture feature tensor can achieve higher recognition rate and better robustness compared to the conventional vector-based classifiers, especially in the case of small sample size.http://dx.doi.org/10.1155/2017/6542348 |
| spellingShingle | Hui Li Xiaofeng Liu Lin Bo Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor International Journal of Rotating Machinery |
| title | Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor |
| title_full | Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor |
| title_fullStr | Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor |
| title_full_unstemmed | Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor |
| title_short | Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor |
| title_sort | fault identification of rotor system based on classifying time frequency image feature tensor |
| url | http://dx.doi.org/10.1155/2017/6542348 |
| work_keys_str_mv | AT huili faultidentificationofrotorsystembasedonclassifyingtimefrequencyimagefeaturetensor AT xiaofengliu faultidentificationofrotorsystembasedonclassifyingtimefrequencyimagefeaturetensor AT linbo faultidentificationofrotorsystembasedonclassifyingtimefrequencyimagefeaturetensor |