Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor
Joint Authors
Source
International Journal of Rotating Machinery
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-03-14
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Li, Hui& Liu, Xiaofeng& Bo, Lin. 2017. Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor. International Journal of Rotating Machinery،Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1169586
Modern Language Association (MLA)
Li, Hui…[et al.]. Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor. International Journal of Rotating Machinery No. 2017 (2017), pp.1-12.
https://search.emarefa.net/detail/BIM-1169586
American Medical Association (AMA)
Li, Hui& Liu, Xiaofeng& Bo, Lin. Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor. International Journal of Rotating Machinery. 2017. Vol. 2017, no. 2017, pp.1-12.
https://search.emarefa.net/detail/BIM-1169586
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1169586