NDLI: Detection of rotor eccentricity faults in closed-loop drive-connected induction motors using an artificial neural network

Content Provider	IEEE Xplore Digital Library
Author	Xianghui Huang Gabetler, T.G. Harley, R.G.
Copyright Year	2004
Description	Author affiliation: Sch. of Electr. & Comput. Eng., Georgia Inst. of Technol., Atlanta, GA, USA (Xianghui Huang; Gabetler, T.G.; Harley, R.G.)
Abstract	This paper focuses on the detection of mixed air gap eccentricity in a closed-loop drive-connected induction motor. It analyzes the distribution of fault signatures, and proposes a diagnostic scheme that monitors both the stator voltage and current space vectors. Since these space vectors are readily available control variables, this detection method does not add any extra cost to the existing instrumentation system of the motor drive. For a drive-connected induction motor, its speed varies widely. The amplitudes of eccentricity related components change non-monotonically with the operating conditions. An artificial neural network (ANN) is used to learn the complicated relationship and estimate corresponding signature amplitudes over a wide range of operating conditions. Experimental results from a three-phase induction motor driven by a commercial vector-controlled drive validate the feasibility of this diagnostic scheme.
Sponsorship	IEEE Power Electon. Soc
Starting Page	913
Ending Page	918
File Size	430087
Page Count	6
File Format	PDF
ISBN	0780383990
ISSN	02759306
DOI	10.1109/PESC.2004.1355541
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-06-20
Publisher Place	Germany
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fault detection Rotors Induction motors Artificial neural networks Functional analysis Stators Voltage Control systems Costs Instruments
Content Type	Text
Resource Type	Article

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