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Author Bo, Lin ♦ Li, Hui ♦ Liu, Xiaofeng
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Hindawi Limited
File Format HTM / HTML
Date Created 2017-04-02
Copyright Year ©2017
Language English
Subject Domain (in LCC) TA1-2040
Subject Keyword Civil engineering ♦ Engineering ♦ Technology
Abstract 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.
ISSN 15423034
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2017-01-01
e-ISSN 1023621X
Journal International Journal of Rotating Machinery
Volume Number 2017


Source: Directory of Open Access Journals (DOAJ)