Publication: Estimación del coeficiente de fricción entre la rueda y el rail
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2016
Defense date
2017-01-19
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Abstract
El coeficiente de fricción entre la rueda y la superficie del rail representa un factor crucial a la hora de mantener altos niveles de aceleración y rendimiento de frenado en los vehículos ferroviarios. Es por esto, que se hace imprescindible el poder conocer en cada instante de tiempo su valor. Dada la dificultad de medir directamente el coeficiente de fricción, la fuerza tangencial (creep force) o el pseudo-deslizamiento (creepage), métodos indirectos basados en observadores de estado son usados con frecuencia. Este Proyecto Fin de Carrera presenta una solución aproximada usando un filtro de Kalman para estimar la fuerza tangencial y los pseudo-deslizamientos entre la rueda y el carril para después identificar el coeficiente de fricción utilizando la curva de deslizamiento (creep force-creepage) estimada. Para simular el sistema motriz se ha construido un modelo matemático incluyendo un motor de CA, una rueda y un rodillo. Sus parámetros están basados en el “test rig” de la Universidad Metropolitana de Manchester. Por comparación entre la fuerza tangencial estimada y los valores teóricos para diferentes coeficientes de fricción se obtiene una serie de residuos. El coeficiente de fricción se estima a partir del error cuadrático medio de los residuos.
The friction coefficient between a railway wheel and rail surface is a crucial factor in maintaining high acceleration and braking performance of railway vehicles thus monitoring this friction coefficient is important. Restricted by the difficulty in directly measuring the friction coefficient, the creep force or creepage, indirect methods using state observers are used more frequently. This paper presents an approach using a Kalman filter to estimate the creep force and creepage between the wheel and rail and then to identify the friction coefficient using the estimated creep force-creepage relationship. A mathematic model including an AC motor, wheel and roller is built to simulate the driving system. The parameters are based on a test rig at Manchester Metropolitan University. The Kalman filter is designed to estimate the friction coefficient based on the measurements of the simulation model. Series of residuals are calculated through the comparison between the estimated creep force and theoretical values of different friction coefficient. Root mean square values of the residuals are used in the friction coefficient estimation.
The friction coefficient between a railway wheel and rail surface is a crucial factor in maintaining high acceleration and braking performance of railway vehicles thus monitoring this friction coefficient is important. Restricted by the difficulty in directly measuring the friction coefficient, the creep force or creepage, indirect methods using state observers are used more frequently. This paper presents an approach using a Kalman filter to estimate the creep force and creepage between the wheel and rail and then to identify the friction coefficient using the estimated creep force-creepage relationship. A mathematic model including an AC motor, wheel and roller is built to simulate the driving system. The parameters are based on a test rig at Manchester Metropolitan University. The Kalman filter is designed to estimate the friction coefficient based on the measurements of the simulation model. Series of residuals are calculated through the comparison between the estimated creep force and theoretical values of different friction coefficient. Root mean square values of the residuals are used in the friction coefficient estimation.
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Mecánica de sólidos, Coeficiente de fricción, Tecnología ferroviaria, Filtro de Kalman