Smart Structures and Systems

  • 제22권5호
  • /
  • Pages.631-641
  • /
  • 2018
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Effective Heterogeneous Data Fusion procedure via Kalman filtering

  • Ravizza, Gabriele (University of Bergamo, Department of Engineering and Applied Sciences) ;
  • Ferrari, Rosalba (University of Bergamo, Department of Engineering and Applied Sciences) ;
  • Rizzi, Egidio (University of Bergamo, Department of Engineering and Applied Sciences) ;
  • Chatzi, Eleni N. (Institute of Structural Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich)
  • 투고 : 2018.07.22
  • 심사 : 2018.11.10
  • 발행 : 2018.11.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

연구 과제 주관 기관 : University of Bergamo

참고문헌

  1. Azam, S.E., Chatzi, E.N. and Papadimitriou, C. (2015), "A dual Kalman filter approach for state estimation via output-only acceleration measurements", Mech. Syst. Signal Pr., 60-61, 866-885. https://doi.org/10.1016/j.ymssp.2015.02.001
  2. Capellari, G., Chatzi, E.N., Mariani, S. and Azam, S.E. (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, Rome, Italy, 10-13 September 2017, Proceedia Engineering, 199, 1864-1869. https://doi.org/10.1016/j.proeng.2017.09.115
  3. Chatzi, E.N. and Smyth, A.W. (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. https://doi.org/10.1002/stc.290
  4. Chatzi, E.N. and Fuggini, C. (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, Delft, Netherlands, October 3-6, 2012, 10, 144-151.
  5. Chatzi, E.N. and Fuggini, C. (2015), "Online correction of drift in Structural Identification using Artificial White Noise observations and an Unscented Kalman Filter", Smart Struct. Syst., 16(2), 296-328.
  6. Chatzis, M.N., Chatzi, E.N. and Triantafyllou, S.P. (2017), "A Discontinuous Extended Kalman Filter for non-smooth dynamic problems", Mech. Syst. Signal Pr., 92, 13-29. https://doi.org/10.1016/j.ymssp.2017.01.021
  7. Cho, S., Yun, C.B. and Sim, S.H. (2015), "Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model", Smart Struct. Syst., 15(3), 645-663. https://doi.org/10.12989/sss.2015.15.3.645
  8. Cho, S., Park, J.W., Bae, M. and Sim, S.H. (2015), "Life-cycle of structural systems: design, assessment, maintenance and management", Proceedings of the 4rd International Symposium on Life-Cycle Civil Engineering, IALCCE 2014, Tokyo, Japan, November 16-19, 2014, 144-151.
  9. Crawley, E.F. and O'Donnell, K.J. (1987), "Force-state mapping identification of non-linear joints", AIAA J., 25(7), 1003-1010. https://doi.org/10.2514/3.9733
  10. Ding, Y. and Guo, L. (2016), "Structural identification based on incomplete measurements with iterative Kalman filter", Struct. Eng. Mech., 59(6), 1037-1054. https://doi.org/10.12989/sem.2016.59.6.1037
  11. Eichstadt, S., Makarava, N. and Elster, C. (2016), "On the evaluation of uncertainties for state estimation with the Kalman filter", Meas. Sci. Technol., 27(12), 27-40.
  12. Ferrari, R., Froio, D., Chatzi, E.N., Gentile, C., Pioldi, F. and Rizzi, E. (2015), "Experimental and numerical investigation for the structural characterization of a historic RC arch bridge", Proceedings of the COMPDYN 2015, 5th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering, Crete Island, Greece, 25-27 May 2015, 2337-2353, online available in Eccomas Proceedia.
  13. Ferrari, R., Pioldi, F., Rizzi, E., Gentile, C., Chatzi, E.N., Klis, R., Serantoni, E. and Wieser, A. (2015), "Heterogeneous sensor fusion for reducing uncertainty in Structural Health Monitoring", Proceedings of the UNCECOMP 2015, 1st ECCOMAS Thematic Conference on International Conference on Uncertainty Quantification in Computational Sciences and Engineering, Crete Island, Greece, 25-27 May 2015, 511-528, online available in Eccomas Proceedia.
  14. Ferrari, R., Pioldi, F., Rizzi, E., Gentile, C., Chatzi, E.N., Serantoni, E. and Wieser, A. (2016), "Fusion of wireless and non-contact technologies for the dynamic testing of a historic RC bridge", Meas. Sci. Technol., 27(12), 1-19.
  15. Ferrari, R., Froio, D., Rizzi, E., Gentile, C. and Chatzi, E.N. (2018), "Model updating of a historic concrete bridge by sensitivity- and global optimization-based Latin Hypercube Sampling", Eng. Struct., 179, 139-160.
  16. Gillijns, S. and DeMoor, B. (2007), "Unbiased minimum-variance linear state estimation for linear discrete-time systems", J. Automatica, 43(1), 111-116. https://doi.org/10.1016/j.automatica.2006.08.002
  17. Gordon, N.J., Salmond, D.J. and Smith, A.F.M. (1993), "Novel approach to non-linear/non-Gaussian Bayesian state estimation", Proceedings of Radar and Signal Processing, 140(2), 107-113. https://doi.org/10.1049/ip-f-2.1993.0015
  18. Hamilton, F., Berry, T. and Sauer, T. (2016), "Ensemble Kalman filtering without a model", Phys. Rev. X, 6(1), 1-12.
  19. Hsieh, C.S. (2000), "Robust two-stage Kalman filters for systems with unknown inputs", IEEE T. Automat. Contr., 45(12), 2374-2378. https://doi.org/10.1109/9.895577
  20. Jiang, S.F., Wang, L.S., Yin, X.Z. and Liu, M. (2005), "Data fusion technique in structural health monitoring", J. Shenyang Jianzhu University (Natural Science), 21(1), 18-22.
  21. Jiang, S.F., Chan, G.K. and Zhang, C.M. (2006), "Data fusion technique and its application in structural health monitoring", Structural Health Monitoring and Intelligent Infrastructure - Proceedings of the 2nd International Conference on Structural Health Monitoring of Intelligent Infrastructure, 1125-1130.
  22. Julier, S.J. and Uhlmann, J.K. (1998), "A non-divergent algorithm in the presence of unknown correlation", Proceedings of the American Control Conference, Albuquerque, New Mexico, USA, June 1997, 2369-2373.
  23. Kalman, R.E. (1960), "A new approach to linear filtering and prediction problems", J. Basic Eng., 82(1), 35-45. https://doi.org/10.1115/1.3662552
  24. Kim, K., Choi, J., Koo, G. and Sohn, H. (2016), "Dynamic displacement estimation by fusing biased high-sampling rate acceleration and low-sampling rate displacement measurements using two-stage Kalman estimator", Smart Struct. Syst., 17(4), 647-667. https://doi.org/10.12989/sss.2016.17.4.647
  25. Kim, K. and Sohn, H. (2017), "Dynamic displacement estimation by fusing LDV and LiDAR measurements via smoothing based Kalman filtering", Mech. Syst. Signal Pr., 82, 339-355. https://doi.org/10.1016/j.ymssp.2016.05.027
  26. Kitanidis, P.K. (1987), "Unbiased minimum-variance linear state estimation", J. Automatica, 23(1), 775-778. https://doi.org/10.1016/0005-1098(87)90037-9
  27. Lee, J.J., Fukuda, Y., Shinozuka, M., Cho, S. and Yun, C.B. (2007), "Development and application of a vision-based displacement measurement system for structural health monitoring of civil structures", Smart Struct. Syst., 3(3), 373-384. https://doi.org/10.12989/sss.2007.3.3.373
  28. Lee, K.G. and Yun, C.B. (2008), "Parameter identification for nonlinear behavior of RC bridge piers using sequential modified extended Kalman filter", Smart Struct. Syst., 4(3), 319-342. https://doi.org/10.12989/sss.2008.4.3.319
  29. Lei, Y., Chen, F. and Zhou, H. (2015), "A two-stage and two-step algorithm for the identification of structural damage and unknown excitations: numerical and experimental studies", Smart Struct. Syst., 15(1), 57-80. https://doi.org/10.12989/sss.2015.15.1.057
  30. Lei, Y., Luo, S. and Su, Y. (2016), "Data fusion based improved Kalman filter with unknown inputs and without collocated acceleration measurements", Smart Struct. Syst., 18(3), 375-387. https://doi.org/10.12989/sss.2016.18.3.375
  31. Lin, X. and Luo, Z.C. (2016), "A new adaptive multi-rate Kalman filter for the data fusion of displacement and acceleration", Chine J. Geophysics, 59(5), 1608-1615.
  32. Liu, L., Zhu, J., Su, Y. and Lei, Y. (2016), "Improved Kalman filter with unknown inputs based on data fusion of partial acceleration and displacement measurements", Smart Struct. Syst., 17(6), 903-915. https://doi.org/10.12989/sss.2016.17.6.903
  33. Papadimitriou, C. and Lombaert, G. (2012), "The effect of prediction error correlation on optimal sensor placement in structural dynamics", Mech. Syst. Signal Pr., 28, 105-127. https://doi.org/10.1016/j.ymssp.2011.05.019
  34. Park, J.W., Sim, S.H. and Jung, H.J. (2013), "Displacement estimation using multimetric data fusion", IEEE/ASME T. Mechatron., 18(6), 1675-1682. https://doi.org/10.1109/TMECH.2013.2275187
  35. Pioldi, F., Ferrari, R. and Rizzi, E. (2015), "Output-only modal dynamic identification of frames by a refined FDD algorithm at seismic input and high damping", Mech. Syst. Signal Pr., 68-69, 265-291.
  36. Pioldi, F., Ferrari, R. and Rizzi, E. (2015), "Earthquake structural modal estimates of multi-storey frames by a refined FDD algorithm", J. Vib. Control, 23(13), 2037-2063. https://doi.org/10.1177/1077546315608557
  37. Pioldi, F., Ferrari, R. and Rizzi, E. (2017), "Seismic FDD modal identification and monitoring of building properties from real strong-motion structural response signals", Struct. Control Health Monit., 24(11), 1-20.
  38. Ravizza, G. (2017), "Dynamic response estimation by heterogeneous data fusion via Kalman filter adaptation, Master Thesis in Building Engineering, (Advisor: E. Rizzi, Co-advisor: R. Ferrari), School of Engineering, University of Bergamo, Italy, March 31, 2017, 186 pages.
  39. Salvi, J. and Rizzi, E. (2014), "Optimum tuning of Tuned Mass Dampers for frame structures under earthquake excitation", Struct. Control Health Monit., 22(4), 707-725. https://doi.org/10.1002/stc.1710
  40. Salvi, J., Rizzi, E., Rustighi, E. and Ferguson, N.S. (2015), "On the optimization of a hybrid Tuned Mass Damper for impulse loading", Smart Mater. Struct., 24(8), 85-100.
  41. Salvi, J. and Rizzi, E. (2016), "Closed-form optimum tuning formulas for passive Tuned Mass Dampers under benchmark excitations", Smart Struct. Syst., 17(2), 231-256. https://doi.org/10.12989/sss.2016.17.2.231
  42. Salvi, J. and Rizzi, E. (2017), "Optimum earthquake-tuned TMDs: seismic performance and new design concept of balance of split effective modal masses", Soil Dyn. Earthq. Eng., 101, 67-80. https://doi.org/10.1016/j.soildyn.2017.05.029
  43. 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
  44. Su, Z., Wang, X., Cheng, L., Long, Y. and Chen, Z. (2009), "Erratum to On selection of data fusion schemes for structural damage evaluation", Struct. Health Monit., 8(6), 573. https://doi.org/10.1177/1475921709107051
  45. Wang, J.F. and Lin, C.C. (2013), "Extracting parameters of TMD and primary structure from the combined system responses", Smart Struct. Syst., 16(5), 937-960. https://doi.org/10.12989/SSS.2015.16.5.937
  46. Wu, B. and Wang, T. (2014), "Model updating with constrained unscented Kalman filter for hybrid testing", Smart Struct. Syst., 14(6), 1105-1129. https://doi.org/10.12989/sss.2014.14.6.1105
  47. Xiao, C., Qu, W. and Tan, D. (2005), "An application of data fusion technology in structural health monitoring and damage identification", Proceedings of SPIE - The International Society for Optical Engineering, 5758(55), 451-461.
  48. Yuqing, Z., Xinfang, L., Fengping, L., Bingtao, S. and Wei, X. (2015), "An online damage identification approach for numerical control machine tools based on data fusion using vibration signals", J. Vib. Control, 21(15), 2925-2936. https://doi.org/10.1177/1077546314545097
  49. Zghal, M., Mevel, L. and Del Moral, P. (2014), "Modal parameter estimation using interacting Kalman filter", Mech. Syst. Signal Pr., 47(1-2), 139-150. https://doi.org/10.1016/j.ymssp.2012.11.005
  50. Zhao, X., Li, M., Song, G. and Xu, J. (2010), "Hierarchical ensemble-based data fusion for structural health monitoring", Smart Mater. Struct., 19(4), 45-59.

피인용 문헌

  1. Reference Structural Investigation on a 19th-Century Arch Iron Bridge Loyal to Design-Stage Conditions vol.14, pp.10, 2018, https://doi.org/10.1080/15583058.2019.1613453