Application of augmented Kalman filter to identify unbalance load of rotor-bearing system: Theory and experiment. (22nd December 2019)

• Record Type: Journal Article
• Authors: Zou, Donglin; Zhao, Han; Liu, Gaoyu; Ta, Na; Rao, Zhushi
• Is Part Of: Journal of sound and vibration. Volume 463(2019)
• Publication Date: 2019-12-22
• Subjects: Rotor -- Augmented Kalman filter (AKF) -- Unbalance -- Identification -- Dynamic balancing -- No trial weights; Sound -- Periodicals; Vibration -- Periodicals; Son -- Périodiques; Vibration -- Périodiques; Sound; Vibration; Periodicals; Electronic journals; 620.205
• DOI: 10.1016/j.jsv.2019.114972
• Languages: English
• ISSNs: 0022-460X

Abstract

Abstract: Rotor dynamic balancing technology with online, no start-stop, no trial weights is an important research topic in rotor dynamics, and its key problem is how to identify the rotor unbalance state online. Rotor unbalance parameter identification is often ill-conditioned mathematically, so a small amount of errors such as measurement and modeling will worsen the identification accuracy. For this reason, a new unbalance loads identification method using the rotor finite element (FE) model combined with augmented Kalman filter (AKF) algorithms is proposed. This method is a deterministic-stochastic, time-domain method and is insensitive to measurement and modeling errors. The research results show that the proposed method can well identify the unbalance parameters online and in real time. Meanwhile, the errors of dynamic model and measurement signal could be considered and effectively filtered out in real time, thus the accuracy of unbalance estimation is improved. Highlights: A new unbalance load identification method based on augmented Kalman filter is proposed. This method can effectively filter out the interference of measurement errors. This method is online, real-time and robust.