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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 국제구조공학회 Smart Structures and Systems, An International Journal Smart Structures and Systems, An International Journal Vol.22 No.5
- 발행연도
- 2018.1
- 수록면
- 631 - 641 (11page)
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초록· 키워드
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This paper outlines a computational procedure for the effective merging of diverse sensor measurements, displacement and acceleration signals in particular, in order to successfully monitor and simulate the current health condition of civil structures under dynamic loadings. In particular, it investigates a Kalman Filter implementation for the Heterogeneous Data Fusion of displacement and acceleration response signals of a structural system toward dynamic identification purposes. The procedure is perspectively aimed at enhancing extensive remote displacement measurements (commonly affected by high noise), by possibly integrating them with a few standard acceleration measurements (considered instead as noise-free or corrupted by slight noise only). Within the data fusion analysis, a Kalman Filter algorithm is implemented and its effectiveness in improving noise-corrupted displacement measurements is investigated. The performance of the filter is assessed based on the RMS error between the original (noise-free, numerically-determined) displacement signal and the Kalman Filter displacement estimate, and on the structural modal parameters (natural frequencies) that can be extracted from displacement signals, refined through the combined use of displacement and acceleration recordings, through inverse analysis algorithms for output-only modal dynamics identification, based on displacements.
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참고문헌 (50)
참고문헌 신청
Azam, S.E / 2015 / A dual Kalman filter approach for state estimation via output-only acceleration measurements / Mech. Syst. Signal Pr 60-61:866~885
Capellari, G / 2017 / Optimal design of sensor networks for damage detection, EURODYN 2017 / Proceedings of the 10th International Conference on Structural Dynamics, Faculty of Civil and Industrial Engineering 199:1864~1869
Chatzi, E.N / 2009 / The unscented Kalman filter and particle filter methods for non-linear structural system identification with non-collocated heterogeneous sensing / Struct. Control Health Monit 16(1):99~123
Chatzi, E.N / 2012 / Structural identification of a super-tall tower by GPS and accelerometer data fusion using a multi-rate Kalman filter / Proceedings of the 3rd International Symposium on Life-Cycle Civil Engineering, IALCCE 2012 :144~151
Eleni N. Chatzi / 2015 / Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman Filter / Smart Structures and Systems, An International Journal 16(2):295~328
Capellari, G / 2017 / Optimal design of sensor networks for damage detection, EURODYN 2017 / Proceedings of the 10th International Conference on Structural Dynamics, Faculty of Civil and Industrial Engineering 199:1864~1869
Chatzi, E.N / 2009 / The unscented Kalman filter and particle filter methods for non-linear structural system identification with non-collocated heterogeneous sensing / Struct. Control Health Monit 16(1):99~123
Chatzi, E.N / 2012 / Structural identification of a super-tall tower by GPS and accelerometer data fusion using a multi-rate Kalman filter / Proceedings of the 3rd International Symposium on Life-Cycle Civil Engineering, IALCCE 2012 :144~151
Eleni N. Chatzi / 2015 / Online correction of drift in structural identification using artificial white noise observations and an unscented Kalman Filter / Smart Structures and Systems, An International Journal 16(2):295~328
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