Model- and Information Theory-Based Diagnostic Method for Induction Motors

[+] Author and Article Information
Sanghoon Lee

Department of Mechanical Engineering, The University of Texas, Austin, Texas 78712-1063onandon7@hotmail.com

Michael D. Bryant

Department of Mechanical Engineering, The University of Texas, Austin, Texas 78712-1063mbryant@mail.utexas.edu

Lalit Karlapalem

Department of Mechanical Engineering, The University of Texas, Austin, Texas 78712-1063ckl@mail.utexas.edu

J. Dyn. Sys., Meas., Control 128(3), 584-591 (Oct 17, 2005) (8 pages) doi:10.1115/1.2232682 History: Received December 03, 2003; Revised October 17, 2005

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.

Copyright © 2006 by American Society of Mechanical Engineers
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Figure 1

Bond graph of an induction motor with state variables (22-23); (a) Stator with three-phase input, (b) stator with two-phase input, with second phase created by a phase delay capacitor, and (c) common rotor for both stators, where the rotor joins the stator at bonds A and B

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Figure 2

Angular position θ and angular velocity ω of rotor axis versus time for values of rotor bar resistance Rr=0.0408Ω, 0.408Ω, and 4.08Ω

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Figure 3

Stator current in the frequency domain for a motor with broken rotor bars, and power spectrum of the machine’s angular velocity response and noise

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Figure 4

Channel capacity for a motor with a broken rotor bar versus resistance of the rotor bar. Part (e) plots the channel capacity, and parts (a) to (d) show corresponding power spectra for explicit points in part (e)

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Figure 5

(a) Rotor velocity versus time for the ideal machine and a machine with shorted stator coil turns, and (b) power spectrum of angular velocity for the shorted machine

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Figure 6

Spectral content of stator currents of phase A for: (a) ideal machine, (b) shorted machine, (c) ideal machine of (29), and (d) shorted machine of (29)

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Figure 7

Channel capacities versus percent change in stator coil resistance for motors with one and two shorted coil(s)

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Figure 8

(a) Power spectra of angular velocity for system with Table 1 “ideal” values (dashed: Ce=5μF), system with degraded phase two capacitor (upper solid: Ce=2μF), and corresponding noise (lower solid). (b) Channel capacity C versus Ce. The dashed line represents information rate R for a 10% error.




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