An ANOVA-Based Fault Diagnosis Approach for Variable Frequency Drive-Fed Induction Motors

• Published in: IEEE transactions on energy conversion Vol. 36; no. 1; pp. 500 - 512
• Main Authors: Shabbir, Md Nasmus Sakib Khan, Liang, Xiaodong, Chakrabarti, Saikat
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-03-2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analysis of variance; ANOVA; Coherence; coherence analysis; DFFT; Electrical measurement; Fast Fourier transformations; Fault diagnosis; Fourier transforms; Harmonic analysis; Induction motors; Mean; Signatures; Squirrel cage motors; Standard deviation; Stator windings; Stators; variable frequency drive (VFD); Variance analysis; Voltage measurement
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2020.3003838

In this article, an analysis of variance (ANOVA)-based fault diagnosis approach using experimental data is proposed for variable frequency drive (VFD)-fed induction motors. Line-to-neutral voltages at the motor terminal and the stator currents, measured from two identical 0.25 HP three-phase squirrel-cage induction motors fed by a voltage source inverter-based low-voltage VFD under healthy and faulty cases, are evaluated. Harmonic spectra of the measured voltage and current are obtained by Discrete Fast Fourier Transform (DFFT). Through the coherence and magnitude consistency analysis, the fundamental and 5th harmonic of the stator current are chosen as "signature frequency components." ANOVA along with the multiway analysis are then applied to signature frequency components, their mean and standard deviation are identified as "fault signatures"; and the p -value from the inter-group analysis of the mean and standard deviation is used to classify faults. To facilitate fault diagnosis for untested motor operating conditions, formulas to calculate fault signatures are derived by surface fitting using tested data.