Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods

Nerea Robles; Danel Madariaga; Fernando Alvarez-Gonzalez; Andres Sierra-Gonzalez

doi:10.1109/ICECCME57830.2023.10252853

Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods

Nerea Robles, Danel Madariaga, Fernando Alvarez-Gonzalez, Andres Sierra-Gonzalez

Clean Powertrain & Electrification

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

Abstract

With the goal of performing condition monitoring and anomaly detection applied to electric machines, tagged datasets are synthetically generated, consisting of time series of electrical and mechanical variables from a 5-phase AC synchronous motor, in different conditions of health or abnormal states. Different off-the-shelf Machine Learning classification methods are then applied to those datasets, to generate models that can identify the different abnormal states from the measured variables. Models' performance is compared, and the best candidate selected for doing real-time anomaly detection and predictive maintenance of similar AC electric motors.

Original language	English
Title of host publication	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350322972
DOIs	https://doi.org/10.1109/ICECCME57830.2023.10252853
Publication status	Published - 2023
Event	2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 - Tenerife, Canary Islands, Spain Duration: 19 Jul 2023 → 21 Jul 2023

Publication series

Name	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

Conference

Conference	2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
Country/Territory	Spain
City	Tenerife, Canary Islands
Period	19/07/23 → 21/07/23

Keywords

anomaly detection
Machine Learning
multiphase motor modeling
predictive maintenance
tagged classification

Access to Document

10.1109/ICECCME57830.2023.10252853

Cite this

Robles, N., Madariaga, D., Alvarez-Gonzalez, F., & Sierra-Gonzalez, A. (2023). Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods. In International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECCME57830.2023.10252853

Robles, Nerea ; Madariaga, Danel ; Alvarez-Gonzalez, Fernando et al. / Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023).

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title = "Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods",

abstract = "With the goal of performing condition monitoring and anomaly detection applied to electric machines, tagged datasets are synthetically generated, consisting of time series of electrical and mechanical variables from a 5-phase AC synchronous motor, in different conditions of health or abnormal states. Different off-the-shelf Machine Learning classification methods are then applied to those datasets, to generate models that can identify the different abnormal states from the measured variables. Models' performance is compared, and the best candidate selected for doing real-time anomaly detection and predictive maintenance of similar AC electric motors.",

keywords = "anomaly detection, Machine Learning, multiphase motor modeling, predictive maintenance, tagged classification",

author = "Nerea Robles and Danel Madariaga and Fernando Alvarez-Gonzalez and Andres Sierra-Gonzalez",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 ; Conference date: 19-07-2023 Through 21-07-2023",

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doi = "10.1109/ICECCME57830.2023.10252853",

language = "English",

series = "International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Robles, N, Madariaga, D, Alvarez-Gonzalez, F & Sierra-Gonzalez, A 2023, Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods. in International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023. International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023, Institute of Electrical and Electronics Engineers Inc., 2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023, Tenerife, Canary Islands, Spain, 19/07/23. https://doi.org/10.1109/ICECCME57830.2023.10252853

Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods. / Robles, Nerea; Madariaga, Danel; Alvarez-Gonzalez, Fernando et al.
International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023. Institute of Electrical and Electronics Engineers Inc., 2023. (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023).

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

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PY - 2023

Y1 - 2023

N2 - With the goal of performing condition monitoring and anomaly detection applied to electric machines, tagged datasets are synthetically generated, consisting of time series of electrical and mechanical variables from a 5-phase AC synchronous motor, in different conditions of health or abnormal states. Different off-the-shelf Machine Learning classification methods are then applied to those datasets, to generate models that can identify the different abnormal states from the measured variables. Models' performance is compared, and the best candidate selected for doing real-time anomaly detection and predictive maintenance of similar AC electric motors.

AB - With the goal of performing condition monitoring and anomaly detection applied to electric machines, tagged datasets are synthetically generated, consisting of time series of electrical and mechanical variables from a 5-phase AC synchronous motor, in different conditions of health or abnormal states. Different off-the-shelf Machine Learning classification methods are then applied to those datasets, to generate models that can identify the different abnormal states from the measured variables. Models' performance is compared, and the best candidate selected for doing real-time anomaly detection and predictive maintenance of similar AC electric motors.

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KW - Machine Learning

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M3 - Conference contribution

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Robles N, Madariaga D, Alvarez-Gonzalez F, Sierra-Gonzalez A. Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods. In International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023. Institute of Electrical and Electronics Engineers Inc. 2023. (International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023). doi: 10.1109/ICECCME57830.2023.10252853

Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods

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Keywords

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