Artículos relacionados a Identifying Bearing Rotordynamic Coefficients using...

Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter - Tapa blanda

Miller, Bard A; Howard, Samuel A

 
9781289221966: Identifying Bearing Rotordynamic Coefficients using an Extended Kalman Filter
Tapa blanda
ISBN 10: 1289221960 ISBN 13: 9781289221966
Editorial: Bibliogov, 2013

Sinopsis

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter s performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

"Sinopsis" puede pertenecer a otra edición de este libro.

Reseña del editor

An Extended Kalman Filter is developed to estimate the linearized direct and indirect stiffness and damping force coefficients for bearings in rotor-dynamic applications from noisy measurements of the shaft displacement in response to imbalance and impact excitation. The bearing properties are modeled as stochastic random variables using a Gauss-Markov model. Noise terms are introduced into the system model to account for all of the estimation error, including modeling errors and uncertainties and the propagation of measurement errors into the parameter estimates. The system model contains two user-defined parameters that can be tuned to improve the filter s performance; these parameters correspond to the covariance of the system and measurement noise variables. The filter is also strongly influenced by the initial values of the states and the error covariance matrix. The filter is demonstrated using numerically simulated data for a rotor-bearing system with two identical bearings, which reduces the number of unknown linear dynamic coefficients to eight. The filter estimates for the direct damping coefficients and all four stiffness coefficients correlated well with actual values, whereas the estimates for the cross-coupled damping coefficients were the least accurate.

"Sobre este título" puede pertenecer a otra edición de este libro.

Editorial
Bibliogov
Año de publicación
2013
Idioma
Inglés
ISBN 10 
1289221960
ISBN 13 
9781289221966
Encuadernación
Tapa blanda
Número de páginas
26
Contacto del fabricante
no disponible
Persona responsable
no disponible