DOI QR코드

DOI QR Code

Experimental validation of Kalman filter-based strain estimation in structures subjected to non-zero mean input

Palanisamy, Rajendra P.;Cho, Soojin;Kim, Hyunjun;Sim, Sung-Han

  • 투고 : 2014.11.30
  • 심사 : 2015.01.13
  • 발행 : 2015.02.25

초록

Response estimation at unmeasured locations using the limited number of measurements is an attractive topic in the field of structural health monitoring (SHM). Because of increasing complexity and size of civil engineering structures, measuring all structural responses from the entire body is intractable for the SHM purpose; the response estimation can be an effective and practical alternative. This paper investigates a response estimation technique based on the Kalman state estimator to combine multi-sensor data under non-zero mean input excitations. The Kalman state estimator, constructed based on the finite element (FE) model of a structure, can efficiently fuse different types of data of acceleration, strain, and tilt responses, minimizing the intrinsic measurement noise. This study focuses on the effects of (a) FE model error and (b) combinations of multi-sensor data on the estimation accuracy in the case of non-zero mean input excitations. The FE model error is purposefully introduced for more realistic performance evaluation of the response estimation using the Kalman state estimator. In addition, four types of measurement combinations are explored in the response estimation: strain only, acceleration only, acceleration and strain, and acceleration and tilt. The performance of the response estimation approach is verified by numerical and experimental tests on a simply-supported beam, showing that it can successfully estimate strain responses at unmeasured locations with the highest performance in the combination of acceleration and tilt.

키워드

response estimation;Kalman filter;data fusion;model error;non-zero mean input

참고문헌

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피인용 문헌

  1. 1. Kalman Filter-based Data Recovery in Wireless Smart Sensor Network for Infrastructure Monitoring vol.20, pp.3, 2016, doi:10.12989/sss.2015.15.2.489
  2. 2. Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms vol.76-77, 2016, doi:10.12989/sss.2015.15.2.489
  3. 3. Reconstruction of Unmeasured Strain Responses in Bottom-fixed Offshore Structures by Multimetric Sensor Data Fusion vol.188, 2017, doi:10.12989/sss.2015.15.2.489
  4. 4. Structural damage identification via response reconstruction under unknown excitation vol.24, pp.8, 2017, doi:10.12989/sss.2015.15.2.489
  5. 5. Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties vol.17, pp.11, 2017, doi:10.12989/sss.2015.15.2.489

과제정보

연구 과제번호 : Development of active-controlled tidal stream generation technology

연구 과제 주관 기관 : Ministry of Oceans and Fisheries