NDLI: Rough set based multi-class fault diagnosis of induction motor using Hilbert Transform

Content Provider	IEEE Xplore Digital Library
Author	Konar, P. Bhawal, S. Saha, M. Sil, J. Chattopadhyay, P.
Copyright Year	2012
Description	Author affiliation: Department of Electrical Engineering, Bengal Engineering and Science University, Shibpur, Howrah, India (Konar, P.; Bhawal, S.; Chattopadhyay, P.) \|\| Department of Computer Science, Bengal Engineering and Science University, Shibpur, Howrah, India (Saha, M.; Sil, J.)
Abstract	The paper proposes a Rough-set Theory based methodology for multi-class fault diagnosis of induction motors using Hilbert Transform (HT). Depending on the motor condition the vibration signals are associated with unique predominant frequency components and instantaneous amplitudes. The axial vibration signals acquired through data acquisition system are split into different mono-components using Kaiser windowed FIR band pass filter. Statistical features of the Hilbert coefficients obtained from the mono-component signals are used as attributes for fault classification. Rough-set theory is successfully applied for dimensionality reduction of the attributes (by 67%) with almost no degradation of classification accuracy. The proposed Rough-set-Hilbert model eliminates the limitation of wavelet based fault diagnosis technique. The computational efficiency of the proposed classifiers increase due to selection of most relevant features, even at a low sampling frequency of 5120 Hz.
Starting Page	337
Ending Page	340
File Size	460944
Page Count	4
File Format	PDF
ISBN	9781467346993
e-ISBN	9781467347006
DOI	10.1109/CODIS.2012.6422207
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-12-28
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault diagnosis Vibrations Wavelet transforms Induction motor Vibration monitoring Induction motors Accuracy Hilbert Transform Rough-set Approximation methods
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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