Reliable Fault Classification of Induction Motors Using Texture Feature Extraction and a Multiclass Support Vector Machine

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 pa...

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Bibliographic Details
Published in:Mathematical problems in engineering 2014-01, Vol.2014 (1)
Main Authors: Uddin, Jia, Kang, Myeongsu, Nguyen, Dinh V., Kim, Jong-Myon
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Comparative analysis
Dimensional analysis
Domain names
Fault detection
Fault diagnosis
Feature extraction
Induction motors
Mathematical analysis
Motors
Performance degradation
Principal components analysis
Radial basis function
State-of-the-art reviews
Support vector machines
Surface layer
Texture
Two dimensional
Vectors (mathematics)
Vibration analysis
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2014/814593