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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Universidad Antonio Nariño
2021
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| author | Sandoval Rodríguez, Camilo Leonardo Barros, Andres Alejandro Herreño Ávila, Sergio Alberto |
| author_facet | Sandoval Rodríguez, Camilo Leonardo Barros, Andres Alejandro Herreño Ávila, Sergio Alberto |
| author_sort | Sandoval Rodríguez, Camilo Leonardo |
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| 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. |
| format | info:eu-repo/semantics/publishedVersion |
| id | repositorio.uan.edu.co-123456789-3932 |
| institution | Repositorio Digital UAN |
| language | spa |
| publishDate | 2021 |
| publisher | Universidad Antonio Nariño |
| record_format | dspace |
| spelling | repositorio.uan.edu.co-123456789-39322024-10-09T22:47:05Z Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance Clasificación automática de patrones de vibraciones mecánicas en maquinaria rotativa afectada por desbalanceo Sandoval Rodríguez, Camilo Leonardo Barros, Andres Alejandro Herreño Ávila, Sergio Alberto 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 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. 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. 2021-06-16T13:53:07Z 2021-06-16T13:53:07Z 2013-10-28 info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:eu-repo/semantics/article http://purl.org/coar/version/c_970fb48d4fbd8a85 http://revistas.uan.edu.co/index.php/ingeuan/article/view/361 http://repositorio.uan.edu.co/handle/123456789/3932 spa http://revistas.uan.edu.co/index.php/ingeuan/article/view/361/301 Acceso abierto Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0) https://creativecommons.org/licenses/by-nc-sa/4.0/ info:eu-repo/semantics/openAccess http://purl.org/coar/access_right/c_abf2 application/pdf Universidad Antonio Nariño 2346-1446 2145-0935 INGE@UAN - TENDENCIAS EN LA INGENIERÍA; Vol. 4 Núm. 7 (2013) |
| spellingShingle | 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 Sandoval Rodríguez, Camilo Leonardo Barros, Andres Alejandro Herreño Ávila, Sergio Alberto Automatic Classification of mechanical vibration patterns in rotating machinery affected by unbalance |
| title | 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_short | 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 |
| 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 |
| url | http://revistas.uan.edu.co/index.php/ingeuan/article/view/361 http://repositorio.uan.edu.co/handle/123456789/3932 |
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