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

^*Autor correspondiente de este trabajo

Powertrain

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

2 Citas (Scopus)

Resumen

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.

Idioma original	Inglés
Título de la publicación alojada	International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9798350322972
DOI	https://doi.org/10.1109/ICECCME57830.2023.10252853
Estado	Publicada - 2023
Evento	2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023 - Tenerife, Canary Islands, Espana Duración: 19 jul 2023 → 21 jul 2023

Serie de la publicación

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

Conferencia

Conferencia	2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023
País/Territorio	Espana
Ciudad	Tenerife, Canary Islands
Período	19/07/23 → 21/07/23

Acceder al documento

10.1109/ICECCME57830.2023.10252853

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

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. En 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).

@inproceedings{226b53f0ccfe43eaac75f9abe256acb4,

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",

year = "2023",

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.",

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

address = "United States",

}

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. En 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, Espana, 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).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods

AU - Robles, Nerea

AU - Madariaga, Danel

AU - Alvarez-Gonzalez, Fernando

AU - Sierra-Gonzalez, Andres

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.

KW - anomaly detection

KW - Machine Learning

KW - multiphase motor modeling

KW - predictive maintenance

KW - tagged classification

UR - http://www.scopus.com/inward/record.url?scp=85174046905&partnerID=8YFLogxK

U2 - 10.1109/ICECCME57830.2023.10252853

DO - 10.1109/ICECCME57830.2023.10252853

M3 - Conference contribution

AN - SCOPUS:85174046905

T3 - International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

Y2 - 19 July 2023 through 21 July 2023

ER -

Robles N, Madariaga D, Alvarez-Gonzalez F, Sierra-Gonzalez A. Anomaly detection of a 5-phase AC electric motor using Machine Learning classification methods. En 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

Resumen

Serie de la publicación

Conferencia

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto