Modulation Signal Bispectrum Analysis of Electric Signals for the Detection and Diagnosis of Compound Faults in Induction Motors with Sensorless Drives

Abdulkarim Shaeboub, Fengshou Gu, Mark Lane, Usama Haba, Zhifei Wu, Andrew Ball

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

As a prime driver, induction motor is the most electric energy consuming component in industry. The exposure of the motor to stator winding asymmetry, combined with broken rotor bar fault significantly increases the temperature and reduces the efficiency and life of the motor. Accurate and timely diagnosis of these faults will help to maintain motors operating under optimal status and avoid excessive energy consumption and severe damages to systems. This paper examines the performance of diagnosing the effect of asymmetry stator winding on broken rotor bar (BRB) faults under closed loop operation modes. It examines the effectiveness of conventional diagnostic features in both motor current and voltage signals using spectrum and modulation signal bispectrum analysis (MSBA). Evaluation results show that the combined faults cause an additional increase in the sideband amplitude and this increase in sideband can be observed in both the current and voltage signals under the sensorless control mode. MSB analysis has a good noise reduction capability and produces a more accurate and reliable diagnosis in that it gives a more correct indication of the fault severity and its location for all operating conditions.
Original languageEnglish
Pages (from-to)252-267
Number of pages16
JournalSystems Science and Control Engineering
Volume5
Issue number1
Early online date2 Jun 2017
DOIs
Publication statusPublished - 2017

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