NDLI: Mechanical fault diagnosis of induction motor using Hilbert pattern

Content Provider	IEEE Xplore Digital Library
Author	Konar, P. Chattopadhyay, P.
Copyright Year	2013
Description	Author affiliation: Dept. of Electr. Eng., Bengal Eng. & Sci. Univ., Howrah, India (Konar, P.; Chattopadhyay, P.)
Abstract	This paper deals with mechanical fault diagnosis in three-phase induction motor from radial vibration measurement. The Hilbert pattern of the 50 Hz mono-component signal extracted from the steady state vibration signature is analyzed and found to contain useful information needed for diagnosing different mechanicals faults. Since Hilbert transform can only be applied to a mono-component signal, Kaiser windowed FIR band pass filter is used to extract the monocomponent signal. Complex analytic signal is generated by using the mono-component signal as the real part and it's Hilbert Transform as the imaginary part. The concept of Hilbert transform for extraction of the instantaneous amplitude and frequency is utilized to extract important fault information from the non-stationary vibration signal and found to be quite efficient. This method does not require the analysis of fault frequency components which are slip dependent. Finally, an automatic diagnosis algorithm is attempted using SVM. The proposed method is almost independent of loading condition of the motor and has consistent performance even in presence of high level of noise.
Starting Page	202
Ending Page	206
File Size	127379
Page Count	5
File Format	PDF
e-ISBN	9781479900831
DOI	10.1109/CATCON.2013.6737498
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-06
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Vibrations Support vector machines Fault diagnosis Induction motors Condition Monitoring Hilbert Transform Noise Induction Motor Rotors Transforms Support Vector Machine (SVMs) Hilbert Pattern
Content Type	Text
Resource Type	Article

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