Clasificación automática de patrones de vibraciones mecánicas en maquinaria rotativa afectada por desbalanceo

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),...

Full description

Saved in:

Bibliographic Details
Main Authors:	Sandoval Rodríguez, Camilo Leonardo, Barros, Andres Alejandro, Herreño, Sergio
Format:	Digital
Language:	spa
Published:	UNIVERSIDAD ANTONIO NARIÑO 2013
Subjects:	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
Online Access:	https://revistas.uan.edu.co/index.php/ingeuan/article/view/361
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.