1106SHM23_Study on an Unscented Kalman filter to identify dynamic parameters of the ball screw feed drive system

Today, the problem of system identification (SI), which includes estimating the dynamic parameters of a complex mechanical system, is an interesting research topic. Previous studies have succeeded in analyzing vibration signals to extract some features to diagnose the condition, which is a special method for identifying ball screw feed drive systems (BFDS). However, these methods require measurement techniques with specialized instruments and complex data processing methods, causing difficulties in their widespread application in practice. This paper presents the application of an unscented Kalman filter (UKF) to estimate the vibrational responses of BFDS. First, a dynamic modeling method for BFDS is proposed to determine dynamic parameters such as mass/inertia, stiffness, and damping. Then, the vibration responses, including displacement, velocity, and acceleration, will be calculated and numerically simulated by the Runge-Kutta 4th order (RK4th). This vibration response is also the input data for estimating states and dynamic parameters using UKF. The combination of the mathematical model and the powerful unscented transformation based on the Kalman filter will allow us to estimate the vibration responses and the dynamic parameters of the system accurately. The feasibility of the UKF method was evaluated by the correlation between the state estimation results and the vibration responses of the RK4th. Besides that, it is possible to evaluate the accuracy of UKF through the error between the data input and the estimated results of the dynamic parameters of BFDS. The preliminary results of this paper demonstrate that the UKF method can be applied to system identification and monitoring of the status of the BFDS system. This approach has the potential to improve the safety and reliability of BFDS systems, as it allows for real-time monitoring and early detection of any potential issues. Further research is needed to fully validate the effectiveness of this method in practical applications.