Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine
This article was published in the Mathematical Problems in Engineering [© 2014 Jia Uddin et al.] and the definite version is available at :http://dx.doi.org/10.1155/2014/814593 The Journal's website is at: https://www.hindawi.com/journals/mpe/2014/814593/
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© 2014 Hindawi Publishing Corporation
2016
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| Accès en ligne: | http://hdl.handle.net/10361/7012 http://dx.doi.org/10.1155/2014/814593 |
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10361-70122016-11-28T09:18:31Z Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine Uddin, Jia Kang, Myeongsu Dish, V. Nguyen Kim, Jong-Myon Department of Computer Science and Engineering, BRAC University Induction motors Principal component analysis Radial basis function networks Systems analysis extures Classification performance This article was published in the Mathematical Problems in Engineering [© 2014 Jia Uddin et al.] and the definite version is available at :http://dx.doi.org/10.1155/2014/814593 The Journal's website is at: https://www.hindawi.com/journals/mpe/2014/814593/ This paper proposes a method for the reliable fault detection and classification of induction motors using two-dimensional (2D) texture features and a multiclass support vector machine (MCSVM). The proposed model first converts time-domain vibration signals to 2D gray images, resulting in texture patterns (or repetitive patterns), and extracts these texture features by generating the dominant neighborhood structure (DNS) map. The principal component analysis (PCA) is then used for the purpose of dimensionality reduction of the high-dimensional feature vector including the extracted texture features due to the fact that the high-dimensional feature vector can degrade classification performance, and this paper configures an effective feature vector including discriminative fault features for diagnosis. Finally, the proposed approach utilizes the one-against-all (OAA) multiclass support vector machines (MCSVMs) to identify induction motor failures. In this study, the Gaussian radial basis function kernel cooperates with OAA MCSVMs to deal with nonlinear fault features. Experimental results demonstrate that the proposed approach outperforms three state-of-the-art fault diagnosis algorithms in terms of fault classification accuracy, yielding an average classification accuracy of 100% even in noisy environments Published 2016-11-28T09:15:04Z 2016-11-28T09:15:04Z 2014 Article Uddin, J., Kang, M., Nguyen, D. V., & Kim, J. -. (2014). Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine. Mathematical Problems in Engineering, 2014 doi:10.1155/2014/814593 1024123X http://hdl.handle.net/10361/7012 http://dx.doi.org/10.1155/2014/814593 en https://www.hindawi.com/journals/mpe/2014/814593/ © 2014 Hindawi Publishing Corporation |
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Brac University |
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Institutional Repository |
| language |
English |
| topic |
Induction motors Principal component analysis Radial basis function networks Systems analysis extures Classification performance |
| spellingShingle |
Induction motors Principal component analysis Radial basis function networks Systems analysis extures Classification performance Uddin, Jia Kang, Myeongsu Dish, V. Nguyen Kim, Jong-Myon Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine |
| description |
This article was published in the Mathematical Problems in Engineering [© 2014 Jia Uddin et al.] and the definite version is available at :http://dx.doi.org/10.1155/2014/814593 The Journal's website is at: https://www.hindawi.com/journals/mpe/2014/814593/ |
| author2 |
Department of Computer Science and Engineering, BRAC University |
| author_facet |
Department of Computer Science and Engineering, BRAC University Uddin, Jia Kang, Myeongsu Dish, V. Nguyen Kim, Jong-Myon |
| format |
Article |
| author |
Uddin, Jia Kang, Myeongsu Dish, V. Nguyen Kim, Jong-Myon |
| author_sort |
Uddin, Jia |
| title |
Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine |
| title_short |
Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine |
| title_full |
Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine |
| title_fullStr |
Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine |
| title_full_unstemmed |
Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine |
| title_sort |
reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine |
| publisher |
© 2014 Hindawi Publishing Corporation |
| publishDate |
2016 |
| url |
http://hdl.handle.net/10361/7012 http://dx.doi.org/10.1155/2014/814593 |
| work_keys_str_mv |
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| _version_ |
1814309614175387648 |