Feature extraction analysis using filter banks for faults classification in induction motors

Jhonattan Bulla; Alvaro D. Orjuela-Canon; Oscar D. Florez

doi:10.1109/IJCNN.2018.8489736

Feature extraction analysis using filter banks for faults classification in induction motors

Jhonattan Bulla
, Alvaro D. Orjuela-Canon
, Oscar D. Florez

Producción científica: Capítulo en Libro/Informe › Contribución a la conferencia

9 Citas (Scopus)

Resumen

Different studies have been worked about induction motor bearings fault detection using digital signal processing and pattern recognition techniques. However, performance of these techniques is related with the use of correct features. This paper presents an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Classification was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23% for six considered classes related with faults in bearings.

Idioma original	Inglés estadounidense
Título de la publicación alojada	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)	9781509060146
DOI	https://doi.org/10.1109/IJCNN.2018.8489736
Estado	Publicada - oct 10 2018
Publicado de forma externa	Sí
Evento	2018 International Joint Conference on Neural Networks, IJCNN 2018 - Rio de Janeiro, Brasil Duración: jul 8 2018 → jul 13 2018

Serie de la publicación

Nombre	Proceedings of the International Joint Conference on Neural Networks
Volumen	2018-July

Conferencia

Conferencia	2018 International Joint Conference on Neural Networks, IJCNN 2018
País/Territorio	Brasil
Ciudad	Rio de Janeiro
Período	7/8/18 → 7/13/18

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 3: Salud y bienestar

Áreas temáticas de ASJC Scopus

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Bulla, J., Orjuela-Canon, A. D., & Florez, O. D. (2018). Feature extraction analysis using filter banks for faults classification in induction motors. En 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings Artículo 8489736 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IJCNN.2018.8489736

Bulla, Jhonattan ; Orjuela-Canon, Alvaro D. ; Florez, Oscar D. / Feature extraction analysis using filter banks for faults classification in induction motors. 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. (Proceedings of the International Joint Conference on Neural Networks).

@inproceedings{fc962e640cda4fa394e264f84ea93a76,

title = "Feature extraction analysis using filter banks for faults classification in induction motors",

abstract = "Different studies have been worked about induction motor bearings fault detection using digital signal processing and pattern recognition techniques. However, performance of these techniques is related with the use of correct features. This paper presents an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Classification was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23\% for six considered classes related with faults in bearings.",

author = "Jhonattan Bulla and Orjuela-Canon, \{Alvaro D.\} and Florez, \{Oscar D.\}",

note = "Funding Information: ACKNOWLEDGMENT Authors thank the Universidad Antonio Nari{\~n}o under project 2017211 and Universidad Distrital Francisco Jose de Caldas for the support and financial assistance in this work. Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 International Joint Conference on Neural Networks, IJCNN 2018 ; Conference date: 08-07-2018 Through 13-07-2018",

year = "2018",

month = oct,

day = "10",

doi = "10.1109/IJCNN.2018.8489736",

language = "English (US)",

series = "Proceedings of the International Joint Conference on Neural Networks",

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

booktitle = "2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings",

address = "United States",

}

Bulla, J, Orjuela-Canon, AD & Florez, OD 2018, Feature extraction analysis using filter banks for faults classification in induction motors. En 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings., 8489736, Proceedings of the International Joint Conference on Neural Networks, vol. 2018-July, Institute of Electrical and Electronics Engineers Inc., 2018 International Joint Conference on Neural Networks, IJCNN 2018, Rio de Janeiro, Brasil, 7/8/18. https://doi.org/10.1109/IJCNN.2018.8489736

Feature extraction analysis using filter banks for faults classification in induction motors. / Bulla, Jhonattan; Orjuela-Canon, Alvaro D.; Florez, Oscar D.
2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. 8489736 (Proceedings of the International Joint Conference on Neural Networks; Vol. 2018-July).

Producción científica: Capítulo en Libro/Informe › Contribución a la conferencia

TY - GEN

T1 - Feature extraction analysis using filter banks for faults classification in induction motors

AU - Bulla, Jhonattan

AU - Orjuela-Canon, Alvaro D.

AU - Florez, Oscar D.

N1 - Funding Information: ACKNOWLEDGMENT Authors thank the Universidad Antonio Nariño under project 2017211 and Universidad Distrital Francisco Jose de Caldas for the support and financial assistance in this work. Publisher Copyright: © 2018 IEEE.

PY - 2018/10/10

Y1 - 2018/10/10

N2 - Different studies have been worked about induction motor bearings fault detection using digital signal processing and pattern recognition techniques. However, performance of these techniques is related with the use of correct features. This paper presents an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Classification was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23% for six considered classes related with faults in bearings.

AB - Different studies have been worked about induction motor bearings fault detection using digital signal processing and pattern recognition techniques. However, performance of these techniques is related with the use of correct features. This paper presents an analysis of the use of filter banks with uniform and nonuniform frequency subbands to features extraction from vibration signals. Classification was developed by an artificial neural network with feedforward connections. Results identifies that the employment of filter banks improve the accuracy in 23% for six considered classes related with faults in bearings.

UR - https://www.scopus.com/pages/publications/85056507635

UR - https://www.scopus.com/pages/publications/85056507635#tab=citedBy

U2 - 10.1109/IJCNN.2018.8489736

DO - 10.1109/IJCNN.2018.8489736

M3 - Conference contribution

AN - SCOPUS:85056507635

T3 - Proceedings of the International Joint Conference on Neural Networks

BT - 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 International Joint Conference on Neural Networks, IJCNN 2018

Y2 - 8 July 2018 through 13 July 2018

ER -

Bulla J, Orjuela-Canon AD, Florez OD. Feature extraction analysis using filter banks for faults classification in induction motors. En 2018 International Joint Conference on Neural Networks, IJCNN 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. 8489736. (Proceedings of the International Joint Conference on Neural Networks). doi: 10.1109/IJCNN.2018.8489736

Feature extraction analysis using filter banks for faults classification in induction motors

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