Off-the-grid compressive sensing for broken-rotor-bar fault detection in squirrel-cage induction motors

In this paper, we propose an off-the-grid compressive sensing based method to detect brokenbar fault in squirrel-cage induction motors. To validate our method, we first build a dynamic model of squirrel-cage induction motor using multi-loop equivalent circuit to simulate motor current under fault conditions. We then develop an off-the-grid compressive sensing algorithm to extract the fault characteristic frequency from the simulated motor current by solving an atomic norm minimization problem. Comparing to other fault detection methods via motor current signature analysis, our method yields high resolution in extracting low-magnitude fault characteristic frequency with only 0.7 second measurements. Simulation results validate our proposed method. 2015 IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS) This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c © Mitsubishi Electric Research Laboratories, Inc., 2015 201 Broadway, Cambridge, Massachusetts 02139 Off-the-Grid Compressive Sensing for Broken-Rotor-Bar Fault Detection in Squirrel-Cage Induction Motors Dehong Liu ∗ Dingguo Lu ∗∗,1 ∗Mitsubishi Electric Research Laboratories, Cambridge, MA 02139 USA (e-mail: [email protected]) ∗∗University of Nebraska-Lincoln, Lincoln, NE 68588, USA (email: [email protected]) Abstract: In this paper, we propose an off-the-grid compressive sensing based method to detect broken-bar fault in squirrel-cage induction motors. To validate our method, we first build a dynamic model of squirrel-cage induction motor using multi-loop equivalent circuit to simulate motor current under fault conditions. We then develop an off-the-grid compressive sensing algorithm to extract the fault characteristic frequency from the simulated motor current by solving an atomic norm minimization problem. Comparing to other fault detection methods via motor current signature analysis, our method yields high resolution in extracting low-magnitude fault characteristic frequency with only 0.7 second measurements. Simulation results validate our proposed method. In this paper, we propose an off-the-grid compressive sensing based method to detect broken-bar fault in squirrel-cage induction motors. To validate our method, we first build a dynamic model of squirrel-cage induction motor using multi-loop equivalent circuit to simulate motor current under fault conditions. We then develop an off-the-grid compressive sensing algorithm to extract the fault characteristic frequency from the simulated motor current by solving an atomic norm minimization problem. Comparing to other fault detection methods via motor current signature analysis, our method yields high resolution in extracting low-magnitude fault characteristic frequency with only 0.7 second measurements. Simulation results validate our proposed method.