Kalman filter utilization in rotor dynamics

Abstract

Unbalance is the most common fault that a rotor system suffers, which result in the vibration. The resulting vibrational analysis is conventionally done by model-based signal processing, in which the measured data is used to extract information like location of the fault, displacement, damping coefficient, stiffness etc and processed to desired form. It is, however, too dependent on the sensor readings and not necessarily accounts the measurement noises. EKF based state estimation is advantageous over the conventional method in estimation of such parameters as it accounts the measurement noise, modelling errors, and can estimate parameters at any desired location from limited number of measured data.

The objective of this study is to use EKF to correct the displacement signal of a model with the faulty parameter to the measured. A FEM model of rotor system was created based on Timoshenko beam theory. The displacement of measured vibration signal at the bearing location for the model vs simulated measurement signal was computed which indicated the deviation between two. EKF was then introduced to correct the model’s signal to the measured. The initial observation was made for the bearing location only but EKF was not able to correct the model. Upon increasing the number of observation points firstly to six and then to ten, EKF was able to correct the model’s signal to the measured. Since it is not always possible to increase the observation point, further studies can be made to compute the EKF variables in more scientific way.