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),...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Sandoval Rodríguez, Camilo Leonardo, Barros, Andres Alejandro, Herreño Ávila, Sergio Alberto
Formato:	info:eu-repo/semantics/publishedVersion
Lenguaje:	spa
Publicado:	Universidad Antonio Nariño 2021
Materias:	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
Acceso en línea:	http://revistas.uan.edu.co/index.php/ingeuan/article/view/361 http://repositorio.uan.edu.co/handle/123456789/3932
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Descripción
Sumario:	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.