Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors

Kawsar Zaman, Shafi Md (2020) Signal processing and graph-based semi-supervised learning-based fault diagnosis for direct online induction motors. Masters thesis, Memorial University of Newfoundland.

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Abstract

In this thesis, fault diagnosis approaches for direct online induction motors are proposed using signal processing and graph-based semi-supervised learning (GSSL). These approaches are developed using experimental data obtained in the lab for two identical 0.25 HP three-phase squirrel-cage induction motors. Various electrical and mechanical single- and multi-faults are applied to each motor during experiments. Three-phase stator currents and three-dimensional vibration signals are recorded simultaneously in each experiment. In this thesis, Power Spectral Density (PSD)-based stator current amplitude spectrum analysis and one-dimensional Complex Continuous Wavelet Transform (CWT)-based stator current time-scale spectrum analysis are employed to detect broken rotor bar (BRB) faults. An effective single- and multi-fault diagnosis approach is developed using GSSL, where discrete wavelet transform (DWT) is applied to extract features from experimental stator current and vibration data. Three GSSL algorithms (Local and global consistency (LGC), Gaussian field and harmonic functions (GFHF), and greedy-gradient max-cut (GGMC)) are adopted and compared in this study. To enable machine learning for untested motor operating conditions, mathematical equations to calculate features for untested conditions are developed using curve fitting and features obtained from experimental data of tested conditions.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/14738
Item ID: 14738
Additional Information: Includes bibliographical references.
Keywords: Fault Diagnosis, Signal Processing, Graph-based Semi-Supervised Learning, Induction Motor, Greedy-Gradient Max-Cut
Department(s): Engineering and Applied Science, Faculty of
Date: October 2020
Date Type: Submission
Library of Congress Subject Heading: Electric motors, Induction--Deterioration--Prevention; Signal processing.

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