Journal of the Earthquake Engineering Society of Korea (한국지진공학회논문집)

Earthquake Engineering Society of Korea (한국지진공학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of Cable Tension Force by ARX Model-Based Virtual Sensing

ARX모델기반 가상센싱을 통한 사장교 케이블의 장력 추정

  • Choi, Gahee (Department of Civil Engineering, Inha University) ;
  • Shin, Soobong (Department of Civil Engineering, Inha University)
  • Received : 2017.01.06
  • Accepted : 2017.09.08
  • Published : 2017.11.01
Download PDF
⟨ Previous Next ⟩

Abstract

Sometimes, it is impossible to install a sensor on a certain location of a structure due to the size of a structure or poor surrounding environments. Even if possible, sensors can be frequently malfunctioned or improperly operated due to lack of adequate maintenance. These kind of problems are solved by the virtual sensing methods in various engineering fields. Virtual sensing technology is a technology that can measure data even though there is no physical sensor. It is expected that this technology can be also applied to the construction field effectively. In this study, a virtual sensing technology based on ARX model is proposed. An ARX model is defined by using the simulated data through a structural analysis rather than by actually measured data. The ARX-based virtual sensing model can be applied to estimate unmeasured response using a transfer function that defines the relationship between two point data. In this study, a simulation and experimental study were carried out to examine the proposed virtual sensing method with a laboratory test on a cable-stayed model bridge. Acceleration measured at a girder is transformed to estimate a cable tension through the ARX model-based virtual sensing.

Keywords

References

  1. Kano M, Fujiwara K. Virtual sensing technology in process industries: trends and challenges revealed by recent industrial applications. J Japanese chem Eng. 2013;46(1):1-17.
  2. Papadimitriou C, Fritzen CP, Kraemer P, Ntotsios E. Fatigue predictions in entire body of metallic structures from a limited number of vibration sensors using Kalman filtering. Struct. Control & Health Monit. 2011 Aug;18(5):554-573. https://doi.org/10.1002/stc.395
  3. Palanisamy RP, Cho S, Sim SH. KSCE Conference; 2014 Oct 22-24; Daegu, Korea: KSCE; c2014.
  4. Wikipedia [Internet]. Virtual sensing. 2016 - [cited 2016 Sep 21]. Available from: https://en.wikipedia.org/wiki/Virtual_sensing
  5. Liu L, Kuo SM, Zhou M. International Conference In Networking, Sensing and Control; 2009 Mar 26-29; okayama, Japan: IEEE; c2009.
  6. Atkinson C, Long TW, Hanzevack EL. SAE Technical Paper Virtual sensing: a neural network-based intelligent performance and emissions prediction system for on-board diagnostics and engine control (US); 1998 Feb. Report No.:980516. c1998.
  7. Liang CY, Srinivasan S, Jacobson EE. Patent(US). U.S. Patent and Trademark Office.; Washington, DC. Patent No.: 6,882,929.
  8. Petersen D, Zander AC, Cazzolato BS, Hansen CH. Optimal virtual sensing for active noise control in a rigid-walled acoustic duct. J Acoust Soc Am. 2005 Oct;118(5):3086- 3093. https://doi.org/10.1121/1.2047127
  9. Ko KJ, Kim WS, Yu SN, Han CS. Virtual Sensor Verification using Neural Network Theory of the Quadruped Robot. J Mech Sci Technol. 2009 Nov;33(11):241-246.
  10. Seren C, Hardier G, Ezerzere, P, Puyou G. Conference on control and fault-Tolerant systems; 2013 Oct 09-11; Nice, France: IEEE; c2013.
  11. Lee BM, Kang UG. Virtual Sensor for Diabetes Meter in U-Health Service. J Bio Sci&Technol. 2014 Dec;6(6):87-96.
  12. Park, S, Chen, D. B, Rho, S. Physical and virtual sensor networks for Energy-Internet of Things. Int J Distrib Sens Networks [Internet]. 2016 Nov [cited 2016 Dec 10];12(11):[about 2 p.]. DOI: 10.1177/1550147716676555.
  13. Ljung L. System identification: Theory for the user. 2nd ed. New Jersey: Prentice Hall; c1999. 609 p.
  14. Kim NS, Park DU, Park YM, Cheung JH. Back Analysis Techniques for the Estimation of Tension Force on Hanger Cables. EESK J. Earthquake Eng. 2007 Jun;11(3):1-10.
  15. Noh MH, Lee SY. Tension Force Identification of Cable Structures using Various Analytical Methods. J Korean Soc Adv. Compos. Struct. 2012 Sep;3(3):38-46. https://doi.org/10.11004/kosacs.2012.3.3.038
  16. Park YS, Choi SM, Lee BG, Kim MJ. Estimation of Stay Cable Tension Using String Vibration Theory. J Korean Soc Hazard Mitigation. 2009 Apr;9(2):17-22.
  17. Zui H, Shinke T, Namita Y. Practical formulas for estimation of cable tension by vibration method. J struct Eng. 1996 Jun;122(6): 651-656. https://doi.org/10.1061/(ASCE)0733-9445(1996)122:6(651)
  18. Jeong HJ, Kim KS, Lee SC. Comparison and Analysis for Evaluation Results of Tension force by cable type of cable-stayed bridge using Measured Control System. KISTEC. 2010 Jun;36:35-49.
  19. Korea Concrete Institute. Guide and practice for safety of existing concrete bridge. Seoul (KR): KCI; c2010. 303 p.
  20. Akaike H, Kitagawa G. The practice of time series analysis. New York: Springer Science & Business Media; c2012. 386 p.