Smart Structures and Systems

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A wireless sensor with data-fusion algorithm for structural tilt measurement

  • Dan Li (China-Pakistan Belt and Road Joint Laboratory on Smart Disaster Prevention of Major Infrastructures, Southeast University) ;
  • Guangwei Zhang (School of Civil Engineering, Southeast University) ;
  • Ziyang Su (School of Civil Engineering, Southeast University) ;
  • Jian Zhang (School of Civil Engineering, Southeast University)
  • Received : 2022.08.18
  • Accepted : 2023.02.28
  • Published : 2023.03.25
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Abstract

Tilt is a key indicator of structural safety. Real-time monitoring of tilt responses helps to evaluate structural condition, enable cost-effective maintenance, and enhance lifetime resilience. This paper presents a prototype wireless sensing system for structural tilt measurement. Long range (LoRa) technology is adopted by the sensing system to offer long-range wireless communication with low power consumption. The sensor integrates a gyroscope and an accelerometer as the sensing module. Although tilt can be estimated from the gyroscope or the accelerometer measurements, these estimates suffer from either drift issue or high noise. To address this challenging issue and obtain more reliable tilt results, two sensor fusion algorithms, the complementary filter and the Kalman filter, are investigated to fully exploit the advantages of both gyroscope and accelerometer measurements. Numerical simulation is carried out to validate and compare the sensor fusion algorithms. Laboratory experiment is conducted on a simply supported beam under moving vehicle load to further investigate the performance of the proposed wireless tilt sensing system.

Keywords

Acknowledgement

The authors wish to express appreciations for the funding support of this research from the Jiangsu Provincial Double-Innovation Doctoral Program (No. JSSCBS20210086). Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author and do not necessarily reflect the view of the sponsors.

References

  1. Alamdari, M.M., Kildashti, K., Samali, B. and Goudarzi, H.V. (2019), "Damage diagnosis in bridge structures using rotation influence line: Validation on a cable-stayed bridge", Eng. Struct., 185, 1-14. https://doi.org/10.1016/j.engstruct.2019.01.124
  2. Arunshankar, J. (2020), "Investigations on state estimation of smart structure systems", Smart Struct. Syst., Int. J., 25(1), 37-45. https://doi.org/10.12989/sss.2020.25.1.037
  3. Augustin, A., Yi, J., Clausen, T. and Townsley, W.M. (2016), "A study of LoRa: Long range & low power networks for the internet of things", Sensors, 16(9), 1466. https://doi.org/10.3390/s16091466
  4. Boutayeb, M., Rafaralahy, H. and Darouach, M. (1997), "Convergence analysis of the extended Kalman filter used as an observer for nonlinear deterministic discrete-time systems", IEEE Transact. Automat. Control, 42(4), 581-586. https://doi.org/10.1109/9.566674
  5. Calcaterra, S., Cesi, C., Di Maio, C., Gambino, P., Merli, K., Vallario, M. and Vassallo, R. (2012), "Surface displacements of two landslides evaluated by GPS and inclinometer systems: A case study in Southern Apennines, Italy", Natural Hazards, 61(1), 257-266. https://doi.org/10.1007/s11069-010-9633-3
  6. Faulkner, K., Brownjohn, J.M.W., Wang, Y. and Huseynov, F. (2020), "Tracking bridge tilt behaviour using sensor fusion techniques", J. Civil Struct. Health Monitor., 10(4), 543-555. https://doi.org/10.1007/s13349-020-00400-9
  7. Fu, Y., Hoang, T., Mechitov, K., Kim, J.R., Zhang, D. and Spencer, B.F.J. (2021), "xShake: Intelligent wireless system for cost-effective real-time seismic monitoring of civil infrastructure", Smart Struct. Syst., Int. J., 28(4), 483-497. https://doi.org/10.12989/sss.2021.28.4.483
  8. Gui, P., Tang, L. and Mukhopadhyay, S. (2015), "MEMS based IMU for tilting measurement: Comparison of complementary and kalman filter based data fusion", Proceedings of 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), Auckland, New Zealand, June.
  9. Ha, D.W., Park, H.S., Choi, S.W. and Kim, Y.S. (2013), "A wireless MEMS-based inclinometer sensor node for structural health monitoring", Sensors, 13(12), 16090-16104. https://doi.org/10.3390/s131216090
  10. Higgins, W.T. (1975), "A comparison of complementary and Kalman filtering", IEEE Transact. Aerosp. Electron. Syst., 3, 321-325. https://doi.org/10.1109/TAES.1975.308081
  11. Huang, H., Xie, X., Zhang, D., Liu, Z. and Lacasse, S. (2019), "Multi-sensor data fusion based assessment on shield tunnel safety", Smart Struct. Syst., Int. J., 24(6), 693-707. https://doi.org/10.12989/sss.2019.24.6.693
  12. Huseynov, F., Kim, C., Obrien, E.J., Brownjohn, J.M.W., Hester, D. and Chang, K.C. (2020), "Bridge damage detection using rotation measurements - Experimental validation", Mech. Syst. Signal Process., 135, 106380. https://doi.org/10.1016/j.ymssp.2019.106380
  13. Kok, M., Hol, J.D. and Schon, T.B. (2014), "An optimization-based approach to human body motion capture using inertial sensors", IFAC Proceedings Volumes, 47(3), 79-85. https://doi.org/10.3182/20140824-6-ZA-1003.02252
  14. Kok, M., Hol, J.D. and Schon, T.B. (2017), Using Inertial Sensors for Position and Orientation Estimation, Now Foundations and Trends.
  15. Kottath, R., Narkhede, P., Kumar, V. and Karar, Vinod and Poddar, Shashi (2017), "Multiple model adaptive complementary filter for attitude estimation", Aerosp. Sci. Technol., 69, 574-581. https://doi.org/10.1016/j.ast.2017.07.011
  16. Ladetto, Q. (2000), "On foot navigation: Continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering", Proceedings of the 13th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2000), Salt Lake City, UT, USA, September.
  17. Li, S., Wang, X., Liu, H., Zhuo, Y., Su, W. and Di, H. (2020), "Dynamic deflection monitoring of high-speed railway bridges with the optimal inclinometer sensor placement", Smart Struct. Syst., Int. J., 26(5), 591-603. https://doi.org/10.12989/sss.2020.26.5.591
  18. Liu, C., Park, J.W., Spencer, B.F., Moon, D.S. and Fan, J. (2017), "Sensor fusion for structural tilt estimation using an acceleration-based tilt sensor and a gyroscope", Smart Mater. Struct., 26(10), 105005. https://doi.org/10.1088/1361-665X/aa84a0
  19. Mahony, R., Hamel, T. and Pflimlin, J.M. (2008), "Nonlinear complementary filters on the special orthogonal group", IEEE Transact. Automat. Control, 53(5), 1203-1218. https://doi.org/10.1109/TAC.2008.923738
  20. Narkhede, P., Joseph Raj, A.N., Kumar, V., Karar, V. and Poddar, S. (2019), "Least square estimation-based adaptive complimentary filter for attitude estimation", Transact. Inst. Measure. Control, 41(1), 235-245. https://doi.org/10.1177/0142331218755234
  21. Narkhede, P., Poddar, S., Walambe, R., Ghinea, G. and Kotecha, K. (2021), "Cascaded complementary filter architecture for sensor fusion in attitude estimation", Sensors, 21(6), 1937. https://doi.org/10.3390/s21061937
  22. Nowicki, M., Wietrzykowski, J. and Skrzypczynski, P. (2015), "Simplicity or flexibility? Complementary Filter vs. EKF for orientation estimation on mobile devices", Proceedings of 2015 IEEE 2nd International Conference on Cybernetics (CYBCONF), Gdynia, Poland, June.
  23. Ozioko, O., Nassar, H. and Dahiya, R. (2021), "3D printed interdigitated capacitor based tilt sensor", IEEE Sensors J., 21(23), 26252-26260. https://doi.org/10.1109/JSEN.2021.3058949
  24. Simon, D. (2006), Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches, John Wiley & Sons, Hoboken, New Jersey, USA.
  25. St-Pierre, M. and Gingras, D. (2004), "Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system", Proceedings of IEEE Intelligent Vehicles Symposium, 2004, Parma, Italy, June.
  26. Su, S., Li, D., Tan, N. and Li, G. (2017), "The study of a novel tilt sensor using magnetic fluid and its detection mechanism", IEEE Sensors J., 17(15), 4708-4715. https://doi.org/10.1109/JSEN.2017.2714689
  27. Shen, Y., Fu, W., Luo, Y., Yun, C.-B., Liu, D., Yang, P., Yang, G. and Zhou, G. (2021), "Implementation of SHM system for Hangzhou East Railway Station using a wireless sensor network", Smart Struct. Syst., Int. J., 27(1), 19-33. https://doi.org/10.12989/sss.2021.27.1.019
  28. Testoni, N., Zonzini, F., Marzani, A., Scarponi, V. and De Marchi, L. (2019), "A tilt sensor node embedding a data-fusion algorithm for vibration-based SHM", Electronics, 8(1), 45. https://doi.org/10.3390/electronics8010045