Title of Paper:An investigation into machine pattern recognition based on time-frequency image feature extraction using a support vector machine

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Date of Publication:2010-01-01

Journal:PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE

Included Journals:SCIE、EI、Scopus

Volume:224

Issue:C4

Page Number:981-994

ISSN No.:0954-4062

Key Words:pattern recognition; time-frequency image; Hilbert time-frequency spectrum; cyclostationarity; gravity central; information entropy; support vector machine

Abstract:In this article, a new method of pattern recognition for machine working conditions is presented that is based on time-frequency image (TFI) feature extraction and support vector machines (SVMs). In this study, the Hilbert time-frequency spectrum (HTFS) is used to construct TFIs because of its good performance in non-stationary and non-linear signal analysis. Cyclostationarity signal analysis is a pre-processing method for improving the performance of the HTFS in the construction of TFIs. Feature extraction for TFIs is investigated in detail to construct a feature vector for pattern recognition. Gravity centre and information entropy of TFIs are used to construct the feature vector for pattern recognition. SVMs are used for different working conditions classification by the constructed feature vector because of its powerful performance even for small samples. In the end, rolling bearing pattern recognition is used as an example to testify the effectiveness of this method. According to the result analysis, it can be concluded that this method will contribute to the development of preventative maintenance.