Author/Authors :
Zurita-Millán, Daniel MCIA Research Center - Department of Electronic Engineering - Technical University of Catalonia (UPC), Spain , Delgado-Prieto, Miguel MCIA Research Center - Department of Electronic Engineering - Technical University of Catalonia (UPC), Spain , Saucedo-Dorantes, Juan José CA Mecatronica - Facultad de Ingenieria - Universidad Autonoma de Queretaro - Campus San Juan del Rio, Mexico , Cariño-Corrales, Jesus Adolfo MCIA Research Center - Department of Electronic Engineering - Technical University of Catalonia (UPC), Spain , Osornio-Rios, Roque A. CA Mecatronica - Facultad de Ingenieria - Universidad Autonoma de Queretaro - Campus San Juan del Rio, Mexico , Ortega-Redondo, J uan Antonio MCIA Research Center - Department of Electronic Engineering - Technical University of Catalonia (UPC), Spain , Romero-Troncoso, Rene de J. CA Telematica - DICIS, Universidad de Guanajuato, Mexico
Abstract :
Vibration monitoring plays a key role in the industrial machinery reliability since it allows enhancing the performance of the machinery under supervision through the detection of failure modes. Thus, vibration monitoring schemes that give information regarding future condition, that is, prognosis approaches, are of growing interest for the scientific and industrial communities. This work proposes a vibration signal prognosis methodology, applied to a rotating electromechanical system and its associated kinematic chain. The method combines the adaptability of neurofuzzy modeling with a signal decomposition strategy to model the patterns of the vibrations signal under different fault scenarios. The model tuning is performed by means of Genetic Algorithms along with a correlation based interval selection procedure. The performance and effectiveness of the proposed method are validated experimentally with an electromechanical test bench containing a kinematic chain. The results of the study indicate the suitability of the method for vibration forecasting in complex electromechanical systems and their associated kinematic chains.
Keywords :
Neurofuzzy Modeling , Signal Decomposition , Rotating Machinery , Vibration Signal Forecasting