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Machine Learning Techniques for Structural Health Monitoring Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Modulation Signal Bispectrum Analysis of Motor Current Signals for Condition Monitoring of Electromechanical Systems

Authors : Funso Otuyemi, Haiyang Li, Fulong Liu, Jiongqi Wang, Fengshou Gu, Andrew D. Ball

Published in: Proceedings of the 13th International Conference on Damage Assessment of Structures

Publisher: Springer Singapore

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Abstract

Induction motor is one of the most widely used prime drivers and electric energy consuming devices in industry. Accurate and timely diagnosis of faults in motors will help to maintain their operating under optimal status and avoid excessive energy consumption and severe damages to systems. In this study, instantaneous motor current and voltage signals (IMCVS) is analyzed by an advanced Modulation Signal Bispectrum (MSB) method to achieve accurate demodulations of Frequency Modulation (FM) and Amplitude Modulation (AM) by minimizing noise influence and enhancing modulation characteristics simultaneously. Firstly, the modulation effects due to motor faults and downstream mechanical components were modelled, thus finding the interaction between AM and FM effect and hence developed a scheme to use the signature of AM and FM jointly for accurate fault diagnosis. Then experimentations were carried out to verify the performance of the proposed scheme in detecting and diagnosing common mechanical faults including Shaft Misalignments(SM), Motor Rotor Broken Bar (BRB), Stator Resistance Imbalance (SRI) and compound BRB with SRI

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Metadata
Title
Modulation Signal Bispectrum Analysis of Motor Current Signals for Condition Monitoring of Electromechanical Systems
Authors
Funso Otuyemi
Haiyang Li
Fulong Liu
Jiongqi Wang
Fengshou Gu
Andrew D. Ball
Copyright Year
2020
Publisher
Springer Singapore
Book
Proceedings of the 13th International Conference on Damage Assessment of Structures

Print ISBN: 978-981-13-8330-4

Electronic ISBN: 978-981-13-8331-1

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-13-8331-1_42

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