Li, Hongkun and Zhou, Peilin and Zhang, Zhixin (2010) An investigation into machine pattern recognition based on time-frequency image feature extraction using a support vector machine. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 224 (4). pp. 981-994. ISSN 0954-4062Full text not available in this repository. (Request a copy from the Strathclyde author)
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.
|Keywords:||pattern recognition, time-frequency image, Hilbert time-frequency spectrum;, cyclostationarity, gravity center, information entropy, support vector machine (SVM), Naval architecture. Shipbuilding. Marine engineering, Mechanical Engineering|
|Subjects:||Naval Science > Naval architecture. Shipbuilding. Marine engineering|
|Department:||Faculty of Engineering > Naval Architecture, Ocean & Marine Engineering|
|Depositing user:||Pure Administrator|
|Date Deposited:||15 Nov 2011 15:47|
|Last modified:||22 Mar 2017 11:53|