Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal

Shady Khalil; Ahmed Al-Shemmery; Kais AbdulMawjood; Sayed Mohammad Kameli; Abdelaziz Abuelrub

doi:10.1109/MetroXRAINE58569.2023.10405822

Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal

Shady Khalil, Ahmed Al-Shemmery, Kais AbdulMawjood, Sayed Mohammad Kameli, Abdelaziz Abuelrub

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Preventing mechanical faults in motors is often impossible, early detection of air gap eccentricity faults in induction motors is critical in preventing damage to the machine. Therefore, designing a reliable, effective fault detection system can help to improve its operation. This paper presents an analysis of static eccentricity fault for induction motors. This paper proposes the use of the empirical mode decomposition (EMD) followed by the implementation of wavelet packet decomposition (WPD) on current signals to extract and identify static eccentricity fault frequency signatures. The accuracy in fault detection and diagnosis of the effects of static airgap eccentricity using stator current based monitoring with WPD based on EMD is experimentally verified.

Original language	English
Title of host publication	2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)
Place of Publication	Milano, Italy
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	752-756
Number of pages	6
ISBN (Electronic)	979-8-3503-0080-2
ISBN (Print)	979-8-3503-0081-9
DOIs	https://doi.org/10.1109/MetroXRAINE58569.2023.10405822
Publication status	Published - 27 Oct 2023
Event	IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering 2023 - Milano, Italy Duration: 25 Oct 2023 → 27 Oct 2023 https://ieee-ims.org/event/ieee-international-conference-metrology-extended-reality-artificial-intelligence-and-neural

Conference

Conference	IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering 2023
Abbreviated title	MetroXRAINE 2023
Country/Territory	Italy
City	Milano
Period	25/10/23 → 27/10/23
Internet address	https://ieee-ims.org/event/ieee-international-conference-metrology-extended-reality-artificial-intelligence-and-neural

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10.1109/MetroXRAINE58569.2023.10405822Licence: Unspecified

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Khalil, S., Al-Shemmery, A., AbdulMawjood, K., Kameli, S. M., & Abuelrub, A. (2023). Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal. In 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE) (pp. 752-756). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/MetroXRAINE58569.2023.10405822

Khalil, Shady ; Al-Shemmery, Ahmed ; AbdulMawjood, Kais et al. / Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal. 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). Milano, Italy : Institute of Electrical and Electronics Engineers (IEEE), 2023. pp. 752-756

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title = "Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal",

abstract = "Preventing mechanical faults in motors is often impossible, early detection of air gap eccentricity faults in induction motors is critical in preventing damage to the machine. Therefore, designing a reliable, effective fault detection system can help to improve its operation. This paper presents an analysis of static eccentricity fault for induction motors. This paper proposes the use of the empirical mode decomposition (EMD) followed by the implementation of wavelet packet decomposition (WPD) on current signals to extract and identify static eccentricity fault frequency signatures. The accuracy in fault detection and diagnosis of the effects of static airgap eccentricity using stator current based monitoring with WPD based on EMD is experimentally verified.",

author = "Shady Khalil and Ahmed Al-Shemmery and Kais AbdulMawjood and Kameli, {Sayed Mohammad} and Abdelaziz Abuelrub",

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doi = "10.1109/MetroXRAINE58569.2023.10405822",

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Khalil, S, Al-Shemmery, A, AbdulMawjood, K, Kameli, SM & Abuelrub, A 2023, Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal. in 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). Institute of Electrical and Electronics Engineers (IEEE), Milano, Italy, pp. 752-756, IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering 2023, Milano, Italy, 25/10/23. https://doi.org/10.1109/MetroXRAINE58569.2023.10405822

Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal. / Khalil, Shady; Al-Shemmery, Ahmed; AbdulMawjood, Kais et al.
2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). Milano, Italy: Institute of Electrical and Electronics Engineers (IEEE), 2023. p. 752-756.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal

AU - Khalil, Shady

AU - Al-Shemmery, Ahmed

AU - AbdulMawjood, Kais

AU - Kameli, Sayed Mohammad

AU - Abuelrub, Abdelaziz

PY - 2023/10/27

Y1 - 2023/10/27

N2 - Preventing mechanical faults in motors is often impossible, early detection of air gap eccentricity faults in induction motors is critical in preventing damage to the machine. Therefore, designing a reliable, effective fault detection system can help to improve its operation. This paper presents an analysis of static eccentricity fault for induction motors. This paper proposes the use of the empirical mode decomposition (EMD) followed by the implementation of wavelet packet decomposition (WPD) on current signals to extract and identify static eccentricity fault frequency signatures. The accuracy in fault detection and diagnosis of the effects of static airgap eccentricity using stator current based monitoring with WPD based on EMD is experimentally verified.

AB - Preventing mechanical faults in motors is often impossible, early detection of air gap eccentricity faults in induction motors is critical in preventing damage to the machine. Therefore, designing a reliable, effective fault detection system can help to improve its operation. This paper presents an analysis of static eccentricity fault for induction motors. This paper proposes the use of the empirical mode decomposition (EMD) followed by the implementation of wavelet packet decomposition (WPD) on current signals to extract and identify static eccentricity fault frequency signatures. The accuracy in fault detection and diagnosis of the effects of static airgap eccentricity using stator current based monitoring with WPD based on EMD is experimentally verified.

U2 - 10.1109/MetroXRAINE58569.2023.10405822

DO - 10.1109/MetroXRAINE58569.2023.10405822

M3 - Conference contribution

SN - 979-8-3503-0081-9

SP - 752

EP - 756

BT - 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE)

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Milano, Italy

T2 - IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering 2023

Y2 - 25 October 2023 through 27 October 2023

ER -

Khalil S, Al-Shemmery A, AbdulMawjood K, Kameli SM, Abuelrub A. Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal. In 2023 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE). Milano, Italy: Institute of Electrical and Electronics Engineers (IEEE). 2023. p. 752-756 doi: 10.1109/MetroXRAINE58569.2023.10405822

Static Eccentricity Fault Analysis in Three-Phase Induction Motors Using Current Signal

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