|Table of Contents|

 Qingcai Yang,Shuying Li,Yunpeng Cao.A Strong Tracking Filtering Approach for Health Estimation of Marine Gas Turbine Engine[J].Journal of Marine Science and Application,2019,(4):542-553.[doi:10.1007/s11804-019-00103-8]
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A Strong Tracking Filtering Approach for Health Estimation of Marine Gas Turbine Engine


A Strong Tracking Filtering Approach for Health Estimation of Marine Gas Turbine Engine
Qingcai Yang Shuying Li Yunpeng Cao
Qingcai Yang Shuying Li Yunpeng Cao
College of Power and Energy Engineering, Harbin Engineering University, Harbin 150001, China
Gas turbineHealth parameter estimationExtended Kalman filterUnscented Kalman filterStrong tracking Kalman filterAnalytical linearization
Monitoring and evaluating the health parameters of marine gas turbine engine help in developing predictive control techniques and maintenance schedules. Because the health parameters are unmeasurable, researchers estimate them only based on the available measurement parameters. Kalman filter-based approaches are the most commonly used estimation approaches; however, the conventional Kalman filter-based approaches have a poor robustness to the model uncertainty, and their ability to track the mutation condition is influenced by historical data. Therefore, in this paper, an improved Kalman filter-based algorithm called the strong tracking extended Kalman filter (STEKF) approach is proposed to estimate the gas turbine health parameters. The analytical expressions of Jacobian matrixes are deduced by non-equilibrium point analytical linearization to address the problem of the conventional approaches. The proposed approach was used to estimate the health parameters of a two-shaft marine gas turbine engine in the simulation environment and was compared with the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). The results show that the STEKF approach not only has a computation cost similar to that of the EKF approach but also outperforms the EKF approach when the health parameters change abruptly and the noise mean value is not zero.


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Received date:2018-03-30;Accepted date:2018-06-25。
Corresponding author:Yunpeng Cao,caoyunpeng@hrbeu.edu.cn
Last Update: 2020-02-04