Sensorless fault diagnosis of induction motors
Kyusung Kim
Parlos, A.G.
Mohan Bharadwaj, R.
Dept. of Mech. Eng., Texas A&M Univ., College Station, TX, USA;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: Oct. 2003
Volume: 50,
Issue: 5
On page(s): 1038- 1051
ISSN: 0278-0046
INSPEC Accession Number: 7757109
Digital Object Identifier: 10.1109/TIE.2003.817693
Current Version Published: 2003-10-07
Abstract
Early detection and diagnosis of incipient faults is desirable for online condition assessment, product quality assurance, and improved operational efficiency of induction motors. In this paper, a speed-sensorless fault diagnosis system is developed for induction motors, using recurrent dynamic neural networks and multiresolution or Fourier-based signal processing for transient or quasi-steady-state operation, respectively. In addition to nameplate information required for the initial system setup, the proposed fault diagnosis system uses only motor terminal voltages and currents. The effectiveness of the proposed diagnosis system in detecting the most widely encountered motor electrical and mechanical faults is demonstrated through extensive staged faults. The developed system is scalable to different power ratings and it has been successfully demonstrated with data from 2.2, 373 and 597 kW induction motors.
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