Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis

William Oñate; Ramon Perez; Gustavo Caiza

doi:10.1007/978-3-030-32033-1_8

Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis

William Oñate, Ramon Perez, Gustavo Caiza

Grupo de Investigación en Electrónica Control y Automatización (GIECA)

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

5 Citas (Scopus)

Resumen

Induction motors are widely used at industrial level and many researches have been conducted to predict faults and ensure their continuous operation. The present study shows a fault detection system based on the Motor Current Signature Analysis (MCSA) technique which is a non-invasive method, and the Fast Fourier Transform (FFT) algorithm to perform spectral analysis on the current to detect specific components that characterize faults in different conditions such as damaged bearings, shorted winding in turns, and broken bars. The tests were conducted using a motor test bench and the results show a spectral value obtained by FFT in the frequency of 300 Hz for faults in bearings of the motors 1 and 2. Results also reveal coincidence of 96.9% and 99.46% compared to the results obtained through the theoretical equations included in the MCSA technique, representing values that are within the fault frequency bands on 1x and 2x respectively.

Idioma original	Inglés
Título de la publicación alojada	Advances in Emerging Trends and Technologies - Volume 2
Editores	Miguel Botto-Tobar, Joffre León-Acurio, Angela Díaz Cadena, Práxedes Montiel Díaz
Editorial	Springer
Páginas	74-84
Número de páginas	11
ISBN (versión impresa)	9783030320324
DOI	https://doi.org/10.1007/978-3-030-32033-1_8
Estado	Publicada - 1 ene. 2020
Evento	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 - quito, Ecuador Duración: 29 may. 2019 → 31 may. 2019

Serie de la publicación

Nombre	Advances in Intelligent Systems and Computing
Volumen	1067
ISSN (versión impresa)	2194-5357
ISSN (versión digital)	2194-5365

Conferencia

Conferencia	1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019
País/Territorio	Ecuador
Ciudad	quito
Período	29/05/19 → 31/05/19

Nota bibliográfica

Publisher Copyright:
© 2020, Springer Nature Switzerland AG.

Acceder al documento

10.1007/978-3-030-32033-1_8

Otros archivos y enlaces

Citar esto

Oñate, W., Perez, R., & Caiza, G. (2020). Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis. En M. Botto-Tobar, J. León-Acurio, A. Díaz Cadena, & P. Montiel Díaz (Eds.), Advances in Emerging Trends and Technologies - Volume 2 (pp. 74-84). (Advances in Intelligent Systems and Computing; Vol. 1067). Springer. https://doi.org/10.1007/978-3-030-32033-1_8

@inproceedings{0ee1637f072847e4958e3ef37b5ed009,

title = "Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis",

abstract = "Induction motors are widely used at industrial level and many researches have been conducted to predict faults and ensure their continuous operation. The present study shows a fault detection system based on the Motor Current Signature Analysis (MCSA) technique which is a non-invasive method, and the Fast Fourier Transform (FFT) algorithm to perform spectral analysis on the current to detect specific components that characterize faults in different conditions such as damaged bearings, shorted winding in turns, and broken bars. The tests were conducted using a motor test bench and the results show a spectral value obtained by FFT in the frequency of 300 Hz for faults in bearings of the motors 1 and 2. Results also reveal coincidence of 96.9% and 99.46% compared to the results obtained through the theoretical equations included in the MCSA technique, representing values that are within the fault frequency bands on 1x and 2x respectively.",

keywords = "FFT, Incipient faults, MSCA, Three-phase motor",

author = "William O{\~n}ate and Ramon Perez and Gustavo Caiza",

note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019 ; Conference date: 29-05-2019 Through 31-05-2019",

year = "2020",

month = jan,

day = "1",

doi = "10.1007/978-3-030-32033-1_8",

language = "English",

isbn = "9783030320324",

series = "Advances in Intelligent Systems and Computing",

publisher = "Springer",

pages = "74--84",

editor = "Miguel Botto-Tobar and Joffre Le{\'o}n-Acurio and {D{\'i}az Cadena}, Angela and {Montiel D{\'i}az}, Pr{\'a}xedes",

booktitle = "Advances in Emerging Trends and Technologies - Volume 2",

}

Oñate, W, Perez, R & Caiza, G 2020, Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis. En M Botto-Tobar, J León-Acurio, A Díaz Cadena & P Montiel Díaz (eds.), Advances in Emerging Trends and Technologies - Volume 2. Advances in Intelligent Systems and Computing, vol. 1067, Springer, pp. 74-84, 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019, quito, Ecuador, 29/05/19. https://doi.org/10.1007/978-3-030-32033-1_8

Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis. / Oñate, William; Perez, Ramon; Caiza, Gustavo.
Advances in Emerging Trends and Technologies - Volume 2. ed. / Miguel Botto-Tobar; Joffre León-Acurio; Angela Díaz Cadena; Práxedes Montiel Díaz. Springer, 2020. p. 74-84 (Advances in Intelligent Systems and Computing; Vol. 1067).

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

TY - GEN

T1 - Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis

AU - Oñate, William

AU - Perez, Ramon

AU - Caiza, Gustavo

PY - 2020/1/1

Y1 - 2020/1/1

N2 - Induction motors are widely used at industrial level and many researches have been conducted to predict faults and ensure their continuous operation. The present study shows a fault detection system based on the Motor Current Signature Analysis (MCSA) technique which is a non-invasive method, and the Fast Fourier Transform (FFT) algorithm to perform spectral analysis on the current to detect specific components that characterize faults in different conditions such as damaged bearings, shorted winding in turns, and broken bars. The tests were conducted using a motor test bench and the results show a spectral value obtained by FFT in the frequency of 300 Hz for faults in bearings of the motors 1 and 2. Results also reveal coincidence of 96.9% and 99.46% compared to the results obtained through the theoretical equations included in the MCSA technique, representing values that are within the fault frequency bands on 1x and 2x respectively.

AB - Induction motors are widely used at industrial level and many researches have been conducted to predict faults and ensure their continuous operation. The present study shows a fault detection system based on the Motor Current Signature Analysis (MCSA) technique which is a non-invasive method, and the Fast Fourier Transform (FFT) algorithm to perform spectral analysis on the current to detect specific components that characterize faults in different conditions such as damaged bearings, shorted winding in turns, and broken bars. The tests were conducted using a motor test bench and the results show a spectral value obtained by FFT in the frequency of 300 Hz for faults in bearings of the motors 1 and 2. Results also reveal coincidence of 96.9% and 99.46% compared to the results obtained through the theoretical equations included in the MCSA technique, representing values that are within the fault frequency bands on 1x and 2x respectively.

KW - FFT

KW - Incipient faults

KW - MSCA

KW - Three-phase motor

UR - http://www.scopus.com/inward/record.url?scp=85075826214&partnerID=8YFLogxK

UR - https://www.mendeley.com/catalogue/6ec4e987-5fdb-3560-970c-84f83a553bf5/

U2 - 10.1007/978-3-030-32033-1_8

DO - 10.1007/978-3-030-32033-1_8

M3 - Conference contribution

AN - SCOPUS:85075826214

SN - 9783030320324

T3 - Advances in Intelligent Systems and Computing

SP - 74

EP - 84

BT - Advances in Emerging Trends and Technologies - Volume 2

A2 - Botto-Tobar, Miguel

A2 - León-Acurio, Joffre

A2 - Díaz Cadena, Angela

A2 - Montiel Díaz, Práxedes

PB - Springer

T2 - 1st International Conference on Advances in Emerging Trends and Technologies, ICAETT 2019

Y2 - 29 May 2019 through 31 May 2019

ER -

Diagnosis of Incipient Faults in Induction Motors Using MCSA and Thermal Analysis

Resumen

Serie de la publicación

Conferencia

Nota bibliográfica

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto