Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance

In this paper, we present an algorithm for automatic classification of vibration patterns on rotating machinery affected by unbalance from spectral analysis. We developed this algorithm using case-based reasoning and various descriptors. The raised descriptors were: The root mean square value (RMS),...

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Autores:
Tipo de recurso:
Article of journal
Fecha de publicación:
2013
Institución:
Universidad Antonio Nariño
Repositorio:
Repositorio UAN
Idioma:
spa
OAI Identifier:
oai:repositorio.uan.edu.co:123456789/10437
Acceso en línea:
https://revistas.uan.edu.co/index.php/ingeuan/article/view/361
https://repositorio.uan.edu.co/handle/123456789/10437
Palabra clave:
Análisis vibracional
reconocimiento de patrones
descriptores de falla en maquina rotativa
espectro de Fourier
Vibrational analysis
pattern recognition
failure descriptors on rotating machine
Fourier spectrum
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License
https://creativecommons.org/licenses/by-nc-sa/4.0
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oai_identifier_str oai:repositorio.uan.edu.co:123456789/10437
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repository_id_str
spelling 2013-10-282024-10-10T02:25:01Z2024-10-10T02:25:01Zhttps://revistas.uan.edu.co/index.php/ingeuan/article/view/361https://repositorio.uan.edu.co/handle/123456789/10437In this paper, we present an algorithm for automatic classification of vibration patterns on rotating machinery affected by unbalance from spectral analysis. We developed this algorithm using case-based reasoning and various descriptors. The raised descriptors were: The root mean square value (RMS), the energy of Fourier spectra, the Higher Order frequency moments and the maximum value of the Fourier spectra. The job was to induce imbalance to a universal motor, taking the vibration signal in time domain by 3300 XL 8mm Proximity sensors and through a data acquisition card NI USB 6008, bringing data to the computer where we implemented a virtual instrument for capturing data and its subsequent transformation to obtain frequency spectrum. Consequently, we developed the algorithm in Matlab to automatically identify the imbalance present in the machine, using the technique of case-based reasoning, based on the calculation of the descriptors and the application of these within the algorithm implemented using the Euclidean distance as part of the decision mechanism among patterns without unbalancing vibration. The results show the RMS as the best performing descriptor for classification showed.En este trabajo, se desarrolla un algoritmo de clasificación automática de los patrones de vibración en maquinaria rotativa afectada por desbalanceo a partir del análisis espectral. En este sentido, se propuso un algoritmo experto usando razonamiento basado en casos y el planteamiento de diversos descriptores de la falla desde el punto de vista de los espectros. Los descriptores planteados fueron: El valor medio cuadrático (RMS), la energía, el valor máximo y los momentos de frecuencia de alto orden (HOFM).  El trabajo entonces consistió en inducir un desbalanceo a un motor universal, tomar la señal de vibración en el dominio del tiempo mediante sensores proximitor y mediante una tarjeta de adquisición de datos USB 6008 de National Instruments, llevar los datos al computador en donde se implementó un Instrumento virtual para la captura de los datos y su posterior transformación para la obtención del espectro de frecuencias. Posteriormente, se desarrolló un algoritmo en Matlab para identificar de manera automática el desbalanceo presente en la maquina, mediante la técnica de razonamiento basado en casos, a partir del cálculo de los descriptores y la aplicación de estos dentro del algoritmo implementado usando la distancia euclidiana como parte del mecanismo de decisión entre patrones de vibración con y sin desbalanceo. Los resultados obtenidos  revelan al RMS como el descriptor que mejor desempeño mostró para la clasificación.application/pdfspaUNIVERSIDAD ANTONIO NARIÑOhttps://revistas.uan.edu.co/index.php/ingeuan/article/view/361/301https://creativecommons.org/licenses/by-nc-sa/4.0http://purl.org/coar/access_right/c_abf2INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 4 Núm. 7 (2013)2346-14462145-0935Análisis vibracionalreconocimiento de patronesdescriptores de falla en maquina rotativaespectro de FourierVibrational analysispattern recognitionfailure descriptors on rotating machineFourier spectrumAutomatic Classification of mechanical vibration patterns in rotating machinery affected by unbalanceClasificación automática de patrones de vibraciones mecánicas en maquinaria rotativa afectada por desbalanceoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_970fb48d4fbd8a85Sandoval Rodríguez, Camilo LeonardoBarros, Andres AlejandroHerreño, Sergio123456789/10437oai:repositorio.uan.edu.co:123456789/104372024-10-14 03:49:12.284metadata.onlyhttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co
dc.title.en-US.fl_str_mv Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
dc.title.es-ES.fl_str_mv Clasificación automática de patrones de vibraciones mecánicas en maquinaria rotativa afectada por desbalanceo
title Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
spellingShingle Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
Análisis vibracional
reconocimiento de patrones
descriptores de falla en maquina rotativa
espectro de Fourier
Vibrational analysis
pattern recognition
failure descriptors on rotating machine
Fourier spectrum
title_short Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
title_full Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
title_fullStr Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
title_full_unstemmed Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
title_sort Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance
dc.subject.es-ES.fl_str_mv Análisis vibracional
reconocimiento de patrones
descriptores de falla en maquina rotativa
espectro de Fourier
topic Análisis vibracional
reconocimiento de patrones
descriptores de falla en maquina rotativa
espectro de Fourier
Vibrational analysis
pattern recognition
failure descriptors on rotating machine
Fourier spectrum
dc.subject.en-US.fl_str_mv Vibrational analysis
pattern recognition
failure descriptors on rotating machine
Fourier spectrum
description In this paper, we present an algorithm for automatic classification of vibration patterns on rotating machinery affected by unbalance from spectral analysis. We developed this algorithm using case-based reasoning and various descriptors. The raised descriptors were: The root mean square value (RMS), the energy of Fourier spectra, the Higher Order frequency moments and the maximum value of the Fourier spectra. The job was to induce imbalance to a universal motor, taking the vibration signal in time domain by 3300 XL 8mm Proximity sensors and through a data acquisition card NI USB 6008, bringing data to the computer where we implemented a virtual instrument for capturing data and its subsequent transformation to obtain frequency spectrum. Consequently, we developed the algorithm in Matlab to automatically identify the imbalance present in the machine, using the technique of case-based reasoning, based on the calculation of the descriptors and the application of these within the algorithm implemented using the Euclidean distance as part of the decision mechanism among patterns without unbalancing vibration. The results show the RMS as the best performing descriptor for classification showed.
publishDate 2013
dc.date.accessioned.none.fl_str_mv 2024-10-10T02:25:01Z
dc.date.available.none.fl_str_mv 2024-10-10T02:25:01Z
dc.date.none.fl_str_mv 2013-10-28
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.coarversion.none.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
format http://purl.org/coar/resource_type/c_6501
status_str publishedVersion
dc.identifier.none.fl_str_mv https://revistas.uan.edu.co/index.php/ingeuan/article/view/361
dc.identifier.uri.none.fl_str_mv https://repositorio.uan.edu.co/handle/123456789/10437
url https://revistas.uan.edu.co/index.php/ingeuan/article/view/361
https://repositorio.uan.edu.co/handle/123456789/10437
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://revistas.uan.edu.co/index.php/ingeuan/article/view/361/301
dc.rights.es-ES.fl_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0
dc.rights.coar.spa.fl_str_mv http://purl.org/coar/access_right/c_abf2
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/4.0
http://purl.org/coar/access_right/c_abf2
dc.format.none.fl_str_mv application/pdf
dc.publisher.es-ES.fl_str_mv UNIVERSIDAD ANTONIO NARIÑO
dc.source.es-ES.fl_str_mv INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 4 Núm. 7 (2013)
dc.source.none.fl_str_mv 2346-1446
2145-0935
institution Universidad Antonio Nariño
repository.name.fl_str_mv Repositorio Institucional UAN
repository.mail.fl_str_mv alertas.repositorio@uan.edu.co
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