NDLI: Vibration Signal Analysis for Electrical Fault Detection of Induction Machine Using Neural Networks

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2007 International Symposium on Information Technology Convergence (ISITC 2007)

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Vibration Signal Analysis for Electrical Fault Detection of Induction Machine Using Neural Networks

Content Provider	IEEE Xplore Digital Library
Author	Hua Su Wang Xi Kil To Chong
Copyright Year	2007
Description	Author affiliation: MIT, Cambridge (Hua Su)
Abstract	This paper presents the development of an online electrical fault detection system that uses neural network (NN) modeling of induction motor in vibration spectra. The short-time Fourier transform (STFT) is used to process the quasi-steady vibration signals for continuous spectra so that the NN model can be trained. The electrical faults are detected from changes in the expectation of modeling errors. Based on experimental observations, the effectiveness of the system is demonstrated, while minimizing the impact of false alarms resulting from power supply imbalance, and it is shown that a robust and automatic electrical fault detection system has been produced.
Starting Page	188
Ending Page	192
File Size	137866
Page Count	5
File Format	PDF
ISBN	0769530451
DOI	10.1109/ISITC.2007.54
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-11-23
Publisher Place	South Korea
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Induction motors Fourier transforms Power supplies Neural networks Signal processing Electrical fault detection Robustness Induction machines Signal analysis Power system modeling
Content Type	Text
Resource Type	Article

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