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| Multi-type structural response reconstruction based on innovation adaptive Kalman filtering algorithm |
| Received:April 18, 2022 Revised:July 16, 2022 |
| View Full Text View/Add Comment Download reader |
| DOI：10.7511/jslx20220418002 |
| KeyWord:unknown noise variance Kalman filtering limited measuring points multi-type response reconstruction |
| Author | Institution |
| 丁怡渊 |
兰州交通大学 机电工程学院, 兰州 |
| 殷红 |
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 |
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| Abstract: |
| In order to improve the problem of low reconstruction precision and even divergence when the process noise variance and measurement noise variance are unknown in the traditional Kalman filter (KF) algorithm,a multi-type response reconstruction method based on the innovation-based adaptive Kalman filter (IAKF) algorithm is proposed.Firstly,the Kalman filter gain matrix and state estimation error covariance matrix are adjusted according to the statistical characteristics of innovation.Then,the acceleration,velocity,displacement and strain of each position of the structure are reconstructed using the measurement data of the acceleration sensors at a limited number of measuring points and combined with the modal method.Finally,the numerical simulation and experimental analysis are carried out on crane truss and a simply supported beam respectively.The results show that the method can effectively adjust the process noise variance and estimate the measurement noise variance,and the reconstructed response time-history curves of the unmeasured points are in good agreement with the calculated response time-history curves or the measured response time-history curves. |
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