Study On The Ball Screw Feed Drive System Identification By Using Unscented Kalman Filter Considering Noise Effect

• Published in: 2024 9th International Conference on Applying New Technology in Green Buildings (ATiGB) pp. 328 - 333
• Main Authors: Nguyen - Quoc, Hung, Pham - Bao, Toan, Nguyen - Quang, Vinh, Huynh - Hoang, Huy, Ngo - Kieu, Nhi, Quang - Truong, Tri
• Format: Conference Proceeding
• Language: English
• Published: IEEE 30-08-2024
• Subjects: Ball screw Feed Drive System (BFDS); Dynamic Modeling; Dynamic Parameters Estimation; Estimation; Feeds; Kalman filters; Mechanical products; Noise; Noise level; Reliability; System identification; System Identification (SI); Unscented Kalman Filter (UKF); Vibration measurement; Vibration Response Estimation; Vibrations
• DOI: 10.1109/ATiGB63471.2024.10717831

System identification (SI), which includes estimating a complex mechanical system's vibration responses and dynamic parameters, is an interesting research topic. Previous studies have succeeded in analyzing vibration signals to diagnose the condition of 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 study presents the application of an Unscented Kalman filter (UKF) to estimate the vibrational responses of BFDS. First, a dynamic modeling method for BFDS is presented to determine dynamic parameters such as mass/inertia, stiffness, and damping. Then, the vibration responses will be simulated by the Runge-Kutta 4th order method (RK4th). These vibration responses added noise are also the measurement input data of UKF for estimating the statespace and dynamic parameters. The feasibility of this study is evaluated by the correlation coefficient of the vibration response estimation results between the UKF method and measurement input. In addition, the UKF estimation accuracy of dynamic parameters can be evaluated by comparing them with the actual values of BFDS. The preliminary results of this study demonstrate that the UKF methods can be applied to system identification and monitoring of the condition 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.