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),...
- Autores:
-
Sandoval Rodríguez, Camilo Leonardo
Barros, Andres Alejandro
Herreño Ávila, Sergio Alberto
- 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:123456789/3932
- Acceso en línea:
- http://revistas.uan.edu.co/index.php/ingeuan/article/view/361
http://repositorio.uan.edu.co/handle/123456789/3932
- Palabra clave:
- Vibrational analysis
pattern recognition
failure descriptors on rotating machine
Fourier spectrum
Análisis vibracional
reconocimiento de patrones
descriptores de falla en maquina rotativa
espectro de Fourier
- Rights
- openAccess
- License
- Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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| 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 Vibrational analysis pattern recognition failure descriptors on rotating machine Fourier spectrum Análisis vibracional reconocimiento de patrones descriptores de falla en maquina rotativa espectro de Fourier |
| 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.creator.fl_str_mv |
Sandoval Rodríguez, Camilo Leonardo Barros, Andres Alejandro Herreño Ávila, Sergio Alberto |
| dc.contributor.author.spa.fl_str_mv |
Sandoval Rodríguez, Camilo Leonardo Barros, Andres Alejandro Herreño Ávila, Sergio Alberto |
| dc.subject.en-US.fl_str_mv |
Vibrational analysis pattern recognition failure descriptors on rotating machine Fourier spectrum |
| topic |
Vibrational analysis pattern recognition failure descriptors on rotating machine Fourier spectrum Análisis vibracional reconocimiento de patrones descriptores de falla en maquina rotativa espectro de Fourier |
| dc.subject.es-ES.fl_str_mv |
Análisis vibracional reconocimiento de patrones descriptores de falla en maquina rotativa espectro de Fourier |
| 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.issued.spa.fl_str_mv |
2013-10-28 |
| dc.date.accessioned.none.fl_str_mv |
2021-06-16T13:53:07Z |
| dc.date.available.none.fl_str_mv |
2021-06-16T13:53:07Z |
| dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
| dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
| dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
| dc.type.coarversion.none.fl_str_mv |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_6501 |
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publishedVersion |
| dc.identifier.none.fl_str_mv |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/361 |
| dc.identifier.uri.none.fl_str_mv |
http://repositorio.uan.edu.co/handle/123456789/3932 |
| url |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/361 http://repositorio.uan.edu.co/handle/123456789/3932 |
| dc.language.iso.spa.fl_str_mv |
spa |
| language |
spa |
| dc.relation.none.fl_str_mv |
http://revistas.uan.edu.co/index.php/ingeuan/article/view/361/301 |
| dc.rights.none.fl_str_mv |
Acceso abierto |
| dc.rights.license.spa.fl_str_mv |
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) |
| dc.rights.uri.spa.fl_str_mv |
https://creativecommons.org/licenses/by-nc-sa/4.0/ |
| dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) Acceso abierto https://creativecommons.org/licenses/by-nc-sa/4.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
| dc.publisher.spa.fl_str_mv |
Universidad Antonio Nariño |
| dc.source.none.fl_str_mv |
2346-1446 2145-0935 |
| dc.source.es-ES.fl_str_mv |
INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 4 Núm. 7 (2013) |
| 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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1814300332926173184 |
| spelling |
Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)Acceso abiertohttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Sandoval Rodríguez, Camilo LeonardoBarros, Andres AlejandroHerreño Ávila, Sergio Alberto2021-06-16T13:53:07Z2021-06-16T13:53:07Z2013-10-28http://revistas.uan.edu.co/index.php/ingeuan/article/view/361http://repositorio.uan.edu.co/handle/123456789/3932In 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 Nariñohttp://revistas.uan.edu.co/index.php/ingeuan/article/view/361/3012346-14462145-0935INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 4 Núm. 7 (2013)Vibrational analysispattern recognitionfailure descriptors on rotating machineFourier spectrumAnálisis vibracionalreconocimiento de patronesdescriptores de falla en maquina rotativaespectro de FourierAutomatic 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/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1info:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85123456789/3932oai:repositorio.uan.edu.co:123456789/39322024-10-09 22:47:05.947https://creativecommons.org/licenses/by-nc-sa/4.0/Acceso abiertometadata.onlyhttps://repositorio.uan.edu.coRepositorio Institucional UANalertas.repositorio@uan.edu.co |