Introduced is a model-based diagnostic system for motors, that also employs concepts of information theory as a health metric. From an existing bond graph of a squirrel cage induction motor, state equations were extracted and simulations performed. Simulated were various cases, including the response of an ideal motor, which functions perfectly to designer’s specifications, and motors with shorted stator coils, a bad phase capacitor, and broken rotor bars. By constructing an analogy between the motor and a communication channel, Shannon’s theorems of information theory were applied to assess functional health. The principal health metric is the channel capacity, which is based on integrals of signal-to-noise ratios. The channel capacity monotonically reduces with degradation of the system, and appears to be an effective discriminator of motor health and sickness. The method was tested via simulations of a three-phase motor; and for experimental verification, a two-phase induction motor was modeled and tested. The method was able to predict impending functional failure, significantly in advance.
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September 2006
Technical Papers
Model- and Information Theory-Based Diagnostic Method for Induction Motors
Sanghoon Lee,
Sanghoon Lee
Department of Mechanical Engineering,
e-mail: onandon7@hotmail.com
The University of Texas
, Austin, Texas 78712-1063
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Michael D. Bryant,
Michael D. Bryant
Department of Mechanical Engineering,
e-mail: mbryant@mail.utexas.edu
The University of Texas
, Austin, Texas 78712-1063
Search for other works by this author on:
Lalit Karlapalem
Lalit Karlapalem
Department of Mechanical Engineering,
e-mail: ckl@mail.utexas.edu
The University of Texas
, Austin, Texas 78712-1063
Search for other works by this author on:
Sanghoon Lee
Department of Mechanical Engineering,
The University of Texas
, Austin, Texas 78712-1063e-mail: onandon7@hotmail.com
Michael D. Bryant
Department of Mechanical Engineering,
The University of Texas
, Austin, Texas 78712-1063e-mail: mbryant@mail.utexas.edu
Lalit Karlapalem
Department of Mechanical Engineering,
The University of Texas
, Austin, Texas 78712-1063e-mail: ckl@mail.utexas.edu
J. Dyn. Sys., Meas., Control. Sep 2006, 128(3): 584-591 (8 pages)
Published Online: October 17, 2005
Article history
Received:
December 3, 2003
Revised:
October 17, 2005
Citation
Lee, S., Bryant, M. D., and Karlapalem, L. (October 17, 2005). "Model- and Information Theory-Based Diagnostic Method for Induction Motors." ASME. J. Dyn. Sys., Meas., Control. September 2006; 128(3): 584–591. https://doi.org/10.1115/1.2232682
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