Smart Structures and Systems

  • 제15권2호
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  • Pages.489-503
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  • 2015
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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Experimental validation of Kalman filter-based strain estimation in structures subjected to non-zero mean input

  • Palanisamy, Rajendra P. (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Cho, Soojin (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Kim, Hyunjun (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Sim, Sung-Han (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST))
  • 투고 : 2014.11.30
  • 심사 : 2015.01.13
  • 발행 : 2015.02.25
⟨ 이전 논문 다음 논문 ⟩

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

과제정보

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

연구 과제 주관 기관 : Ministry of Oceans and Fisheries

참고문헌

  1. Bavdekar, V.A., Anjali, P.D. and Sachin, C.P. (2011), "Identification of process and measurement noise covariance for state and parameter estimation using Extended Kalman Filter", J. Process Contr., 21(4), 585-601. https://doi.org/10.1016/j.jprocont.2011.01.001
  2. Cho, S., Sim, S.-H., Park, J.W. and Lee, J. (2014), "Extension of indirect displacement estimation method using acceleration and strain to various types of beam structures", Smart Struct. Syst., 14(4), 699-718. https://doi.org/10.12989/sss.2014.14.4.699
  3. Gresil, M., Yu, L., Shen, Y. and Giurgiutiu, V. (2013), "Predictive model of fatigue crack detection in thick bridge steel structures with piezoelectric wafer active sensors", Smart Struct. Syst., 12(2), 97-119. https://doi.org/10.12989/sss.2013.12.2.097
  4. Fatemi, A. and Yang, L. (1998), "Cumulative fatigue damage and life prediction theories: a survey of the state of the art for homogeneous materials", Int. J. Fatigue., 20(1), 9-34. https://doi.org/10.1016/S0142-1123(97)00081-9
  5. Hjelm, H.P., Brincker, R., Graugaard-Jensen, J. and Munch, K. (2005), "Determination of stress histories in structures by natural input modal analysis", Proceedings of the 23rd Conference and Exposition on Structural Dynamics (IMACXXIII), Orlando, Florida, USA, February.
  6. Iliopoulos, A.N., Devriendt, C., Iliopoulos, S.N. and Van Hemelrijck, D. (2014), "Continuous fatigue assessment of offshore wind turbines using a stress prediction technique", Proc. of SPIE, Health Monitoring of Structural and Biological Systems 2014,Vol. 9064 90640S-1,San Diego, California, USA, March.
  7. Jo, H. (2013), Multi-scale structural health monitoring using wireless smart sensors, PhD dissertation, University of Illinois at Urbana-Champaign.
  8. Jo, H., and Spencer, B.F. (2014), "Multi-Metric model based structure health monitoring", Proc. of SPIE, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2014, San Diego, California, USA, March.
  9. Kalman, R.E. (1960), "A new approach to linear filtering and prediction problem", J. Basic Engg., 82(1), 35-45. https://doi.org/10.1115/1.3662552
  10. Li, H.N., Yi, T.H., Ren, L., Li, D.S. and Huo, L.S. (2014), "Reviews on innovations and applications in structural health monitoring for infrastructures", Struct. Monit. Maint., 1(1), 1-45. https://doi.org/10.12989/SMM.2014.1.1.001
  11. Lourens, E., Papadimitriou, C., Gillijns, S., Reynders, E., Roeck, G.D. and Lombaert, G. (2012), "Joint input-response estimation for structural systems based on reduced-order models and vibration data from a limited number of sensors", Mech. Syst. Signal Pr., 29, 310-327. https://doi.org/10.1016/j.ymssp.2012.01.011
  12. Miner, M.A. (1945), "Cumulative damage in fatigue", J. Appl. Mech. - T ASME, 12(3), 159-164.
  13. Palmgren, A. (1924), "Die Lebensdauer von Kugallagern," VDI-Zeitschrift., 68(14), 339-341.
  14. Papadimitriou, C., Fritzen, C.P., Kraemer, P. and Ntotsios, E. (2009), "Fatigue lifetime estimation in structures using ambient vibration measurements", Proceedings of the ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Rhodes, Greece, June.
  15. Papadimitriou, C., Fritzen, C.P., Kraemer, P. and Ntotsios, E. (2010), "Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering", Struct. Control Health Monit., 18(5), 554-573. https://doi.org/10.1002/stc.395
  16. Park, J.W., Sim, S.-H., and Jung, H.J. (2013), "Displacement estimation using multimetric data fusion", IEEE T. Mech. - ASME., 18(6), 1675-1682. https://doi.org/10.1109/TMECH.2013.2275187
  17. Park, J.W., Sim, S.-H. and Jung, H.J. (2014), "Wireless displacement sensing system for bridges using multi-sensor fusion", Smart Mater. Struct., 23(4), 045022. https://doi.org/10.1088/0964-1726/23/4/045022
  18. Shimokawa, T. and Tanaka S. (1980), "A statistical consideration of Miner's rule", Int. J. Fatigue., 2(4), 165-170. https://doi.org/10.1016/0142-1123(80)90044-4
  19. Smyth, A. and Wu, M. (2007), "Multi-rate Kalman filtering for the data fusion of displacement and acceleration response measurements in dynamic system monitoring", Mech. Syst. Signal Pr., 21(2), 706-723. https://doi.org/10.1016/j.ymssp.2006.03.005
  20. Soman, R.N., Onoufriou, T., Kyriakides, M.A., Votsis, R.A. and Chrysostomou, C.Z. (2014), "Multi-type, multi-sensor placement optimization for structural health monitoring of long span bridges", Smart Struct. Syst., 14(1), 55-70. https://doi.org/10.12989/sss.2014.14.1.055
  21. Wieghaus, K.T., Hurlebaus, S. and Mander, J.B. (2014), "Effectiveness of strake installation for traffic signal structure fatigue mitigation", Struct Monit. Maint., 1(4), 393-409. https://doi.org/10.12989/smm.2014.1.4.393
  22. Yazid, E., Mohd, S.L., and Setyamartan, P. (2012),"Time-varying spectrum estimation of offshore structure response based on a time-varying autoregressive model", J. Appl. Sci., 12(23), 2383-2389. https://doi.org/10.3923/jas.2012.2383.2389
  23. Zhou, Y.E. (2006), "Assessment of bridge remaining fatigue life through field strain measurement", J. Bridge Eng. - ASCE., 11(6), 737-744. https://doi.org/10.1061/(ASCE)1084-0702(2006)11:6(737)

피인용 문헌

  1. Kalman Filter-based Data Recovery in Wireless Smart Sensor Network for Infrastructure Monitoring vol.20, pp.3, 2016, https://doi.org/10.11112/jksmi.2016.20.3.042
  2. Dynamic strain estimation for fatigue assessment of an offshore monopile wind turbine using filtering and modal expansion algorithms vol.76-77, 2016, https://doi.org/10.1016/j.ymssp.2016.01.004
  3. Reconstruction of Unmeasured Strain Responses in Bottom-fixed Offshore Structures by Multimetric Sensor Data Fusion vol.188, 2017, https://doi.org/10.1016/j.proeng.2017.04.461
  4. Structural damage identification via response reconstruction under unknown excitation vol.24, pp.8, 2017, https://doi.org/10.1002/stc.1953
  5. Model-Based Heterogeneous Data Fusion for Reliable Force Estimation in Dynamic Structures under Uncertainties vol.17, pp.11, 2017, https://doi.org/10.3390/s17112656
  6. A sensor fault detection strategy for structural health monitoring systems vol.20, pp.1, 2015, https://doi.org/10.12989/sss.2017.20.1.043
  7. Quasi real-time and continuous non-stationary strain estimation in bottom-fixed offshore structures by multimetric data fusion vol.23, pp.1, 2019, https://doi.org/10.12989/sss.2019.23.1.061
  8. KF-Based Multiscale Response Reconstruction under Unknown Inputs with Data Fusion of Multitype Observations vol.32, pp.4, 2015, https://doi.org/10.1061/(asce)as.1943-5525.0001031
  9. Investigations on state estimation of smart structure systems vol.25, pp.1, 2020, https://doi.org/10.12989/sss.2020.25.1.037
  10. Structural sensing with deep learning: Strain estimation from acceleration data for fatigue assessment vol.35, pp.12, 2015, https://doi.org/10.1111/mice.12565
  11. Full-field strain estimation of subsystems within time-varying and nonlinear systems using modal expansion vol.153, pp.None, 2021, https://doi.org/10.1016/j.ymssp.2020.107505
  12. Advanced optimal sensor placement for Kalman-based multiple-input estimation vol.160, pp.None, 2021, https://doi.org/10.1016/j.ymssp.2021.107830