NDLI: Reliable Fault Diagnosis of Multiple Induction Motor Defects Using a 2-D Representation of Shannon Wavelets

Content Provider	IEEE Xplore Digital Library
Author	Myeongsu Kang Jong-Myon Kim
Copyright Year	1965
Abstract	This paper proposes an approach for a 2-D representation of Shannon wavelets for highly reliable fault diagnosis of multiple induction motor defects. Since the wavelet transform is efficient for analyzing non-stationary and non-deterministic vibration signals, this paper utilizes wavelet coefficients deduced from the Shannon mother wavelet function with varying dilation and translation parameters to create 2-D gray-level images. Using the resulting images and their associated texture characteristics, this paper extracts features by generating global neighborhood structure maps, which are used to extract global image features. The texture features are then used as inputs in one-against-all multi-class support vector machines to identify faults in the induction machine. To evaluate the performance of the proposed approach, it is compared with five conventional state-of-the-art algorithms in terms of classification accuracy. In addition, this paper explores the robustness of the proposed approach in noisy environments by adding white Gaussian noise to the acquired vibration signals. The experimental results indicate that the proposed approach outperforms conventional algorithms in terms of the classification accuracy. Moreover, the proposed approach achieves higher classification accuracy, even in noisy environments.
Sponsorship	IEEE Magnetics Society
Starting Page	1
Ending Page	13
Page Count	13
File Size	4614311
File Format	PDF
ISSN	00189464
Volume Number	50
Issue Number	10
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Induction motors Vibrations Feature extraction Rotors Wavelet transforms Fault diagnosis Wavelet analysis wavelet transform Fault classification global neighborhood structure (GNS) maps induction machine multi-class support vector machines (MCSVMs)
Content Type	Text
Resource Type	Article
Subject	Electronic, Optical and Magnetic Materials Electrical and Electronic Engineering

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