Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 9, Issue 4, Pages 2274-2283Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2013.2242084
Keywords
Air-gap torque; broken bars; fault diagnostics; Gaussian mixture models (GMMs); induction machines; monitoring of induction motors; reconstructed phase space; speed estimator; torque estimator
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Funding
- National Science Foundation [ECS-0322974]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1028348] Funding Source: National Science Foundation
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A robust method to monitor the operating conditions of induction motors is presented. This method utilizes the data analysis of the air-gap torque profile in conjunction with a Bayesian classifier to determine the operating condition of an induction motor as either healthy or faulty. This method is trained offline with datasets generated either from an induction motor modeled by a time-stepping finite-element (TSFE) method or experimental data. This method can effectively monitor the operating conditions of induction motors that are different in frame/class, ratings, or design from the motor used in the training stage. Such differences can include the level of load torque and operating frequency. This is due to a novel air-gap torque normalization method introduced here, which leads to a motor fault classification process independent of these parameters and with no need for prior information about the motor being monitored. The experimental results given in this paper validate the robustness and efficacy of this method. Additionally, this method relies exclusively on data analysis of motor terminal operating voltages and currents, without relying on complex motor modeling or internal performance parameters not readily available.
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