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| Structural response reconstruction of adaptive window length improved Sage-Husa Kalman filter |
| Received:September 26, 2023 Revised:November 04, 2023 |
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| DOI：10.7511/jslx20230926003 |
| KeyWord:sage-husa kalman filter structural response reconstruction noise variance estimation UD decomposition |
| Author | Institution |
| 路金涛 |
兰州交通大学 机电工程学院, 兰州 |
| 彭珍瑞 |
兰州交通大学 机电工程学院, 兰州 ;兰州理工大学 机电工程学院, 兰州 |
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| Abstract: |
| In order to solve the problem that the accuracy of structural response reconstruction is reduced due to the empirical selection of window length in the fixed window estimation noise variance Kalman filter method,a structural response reconstruction method based on adaptive window length improved Sage-Husa Kalman filter(ISHKF)is proposed.Firstly,the UD decomposition is applied to the Kalman filter prior error covariance matrix to ensure its positive definiteness,and the window length is adaptively adjusted according to the measurement prior error,and then the Sage-Husa time-varying noise estimator is used to realize the synchronous adjustment of measurement noise and system noise.Secondly,according to the known partial acceleration responses,combined with the modal superposition method,the acceleration,velocity and displacement responses are reconstructed.Finally,the method is verified by numerical simulations of a C-bracket of a certain Electric Multiple Unit gearbox and the two-dimensional truss structure,and the test of a simply supported beam.The results show that the proposed method can realize the structural response reconstruction under adaptive window length unknown noise variance estimation,and the reconstruction accuracy is higher than that by the moving window Kalman filter method. |
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