Machine learning approaches for fault detection and diagnosis of induction motors

Lamia Belguesmi; Mansour Hajji; Majdi Mansouri; Mohamed Faouzi Harkat; Abdelmalek Kouadri; Hazem Nounou; Mohamed Nounou

doi:10.1109/SSD49366.2020.9364240

Machine learning approaches for fault detection and diagnosis of induction motors

Lamia Belguesmi, Mansour Hajji, Majdi Mansouri, Mohamed Faouzi Harkat, Abdelmalek Kouadri, Hazem Nounou, Mohamed Nounou

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

8 Citations (Scopus)

Abstract

This paper deals with the problem of monitoring of induction motors IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.

Original language	English
Title of host publication	Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	692-698
Number of pages	7
ISBN (Electronic)	9781728110806
DOIs	https://doi.org/10.1109/SSD49366.2020.9364240
Publication status	Published - 20 Jul 2020
Externally published	Yes
Event	17th International Multi-Conference on Systems, Signals and Devices, SSD 2020 - Sfax, Tunisia Duration: 20 Jul 2020 → 23 Jul 2020

Publication series

Name	Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020

Conference

Conference	17th International Multi-Conference on Systems, Signals and Devices, SSD 2020
Country/Territory	Tunisia
City	Sfax
Period	20/07/20 → 23/07/20

Keywords

Induction motor
fault classification
fault diagnosis
feature extraction
machine learning (ML)
principal component analysis (PCA)

Access to Document

10.1109/SSD49366.2020.9364240

Cite this

Belguesmi, L., Hajji, M., Mansouri, M., Harkat, M. F., Kouadri, A., Nounou, H., & Nounou, M. (2020). Machine learning approaches for fault detection and diagnosis of induction motors. In Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020 (pp. 692-698). Article 9364240 (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSD49366.2020.9364240

Belguesmi, Lamia ; Hajji, Mansour ; Mansouri, Majdi et al. / Machine learning approaches for fault detection and diagnosis of induction motors. Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 692-698 (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020).

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title = "Machine learning approaches for fault detection and diagnosis of induction motors",

abstract = "This paper deals with the problem of monitoring of induction motors IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.",

keywords = "Induction motor, fault classification, fault diagnosis, feature extraction, machine learning (ML), principal component analysis (PCA)",

author = "Lamia Belguesmi and Mansour Hajji and Majdi Mansouri and Harkat, {Mohamed Faouzi} and Abdelmalek Kouadri and Hazem Nounou and Mohamed Nounou",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020 ; Conference date: 20-07-2020 Through 23-07-2020",

year = "2020",

month = jul,

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doi = "10.1109/SSD49366.2020.9364240",

language = "English",

series = "Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020",

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Belguesmi, L, Hajji, M, Mansouri, M, Harkat, MF, Kouadri, A, Nounou, H & Nounou, M 2020, Machine learning approaches for fault detection and diagnosis of induction motors. in Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020., 9364240, Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, Institute of Electrical and Electronics Engineers Inc., pp. 692-698, 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020, Sfax, Tunisia, 20/07/20. https://doi.org/10.1109/SSD49366.2020.9364240

Machine learning approaches for fault detection and diagnosis of induction motors. / Belguesmi, Lamia; Hajji, Mansour; Mansouri, Majdi et al.
Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 692-698 9364240 (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020).

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

TY - GEN

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AU - Belguesmi, Lamia

AU - Hajji, Mansour

AU - Mansouri, Majdi

AU - Harkat, Mohamed Faouzi

AU - Kouadri, Abdelmalek

AU - Nounou, Hazem

AU - Nounou, Mohamed

PY - 2020/7/20

Y1 - 2020/7/20

N2 - This paper deals with the problem of monitoring of induction motors IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.

AB - This paper deals with the problem of monitoring of induction motors IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.

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KW - fault classification

KW - fault diagnosis

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ER -

Belguesmi L, Hajji M, Mansouri M, Harkat MF, Kouadri A, Nounou H et al. Machine learning approaches for fault detection and diagnosis of induction motors. In Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 692-698. 9364240. (Proceedings of the 17th International Multi-Conference on Systems, Signals and Devices, SSD 2020). doi: 10.1109/SSD49366.2020.9364240

Machine learning approaches for fault detection and diagnosis of induction motors

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Keywords

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