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Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine
Joint Authors
Nguyen, Dinh V.
Uddin, Jia
Kang, Myeongsu
Kim, Jong-Myon
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-28
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Uddin, Jia& Kang, Myeongsu& Nguyen, Dinh V.& Kim, Jong-Myon. 2014. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-500216
Modern Language Association (MLA)
Uddin, Jia…[et al.]. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine. Mathematical Problems in Engineering No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-500216
American Medical Association (AMA)
Uddin, Jia& Kang, Myeongsu& Nguyen, Dinh V.& Kim, Jong-Myon. Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-500216
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-500216