Smart Structures and Systems

  • 제14권2호
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  • Pages.145-158
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  • 2014
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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SVR model reconstruction for the reliability of FBG sensor network based on the CFRP impact monitoring

  • Zhang, Xiaoli (College of Physics and Electronic Engineering, Xinyang Normal University) ;
  • Liang, Dakai (The State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics) ;
  • Zeng, Jie (The State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics) ;
  • Lu, Jiyun (The State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics)
  • 투고 : 2012.07.26
  • 심사 : 2013.07.30
  • 발행 : 2014.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

The objective of this study is to improve the survivability and reliability of the FBG sensor network in the structural health monitoring (SHM) system. Therefore, a model reconstruction soft computing recognition algorithm based on support vector regression (SVR) is proposed to achieve the high reliability of the FBG sensor network, and the grid search algorithm is used to optimize the parameters of SVR model. Furthermore, in order to demonstrate the effectiveness of the proposed model reconstruction algorithm, a SHM system based on an eight-point fiber Bragg grating (FBG) sensor network is designed to monitor the foreign-object low velocity impact of a CFRP composite plate. Simultaneously, some sensors data are neglected to simulate different kinds of FBG sensor network failure modes, the predicting results are compared with non-reconstruction for the same failure mode. The comparative results indicate that the performance of the model reconstruction recognition algorithm based on SVR has more excellence than that of non-reconstruction, and the model reconstruction algorithm almost keeps the consistent predicting accuracy when no sensor, one sensor and two sensors are invalid in the FBG sensor network, thus the reliability is improved when there are FBG sensors are invalid in the structural health monitoring system.

키워드

참고문헌

  1. Akay, M.F. and Ipek, A. (2010), "Predicting the performance measures of an optical distributed shared memory multiprocessor by using support vector regression", Expert Syst. Appl., 37(9), 6293-6301. https://doi.org/10.1016/j.eswa.2010.02.092
  2. Andres, A.T., Alberto, A.S. and Sen, R.J. (1998), "Galvanic interaction between carbon fiber reinforced plastic (CFRP) composites and steel in chloride contaminated concrete", Proceedings of the 1998 NACE International Conference, Houston, Aug.
  3. Ankit, B.G., JoshiJ, B., Valadi, K.J. and Bhaskar, D.K. (2007), "Development of support vector regression (SVR)-based correlation for prediction of overall gas hold-up in bubble column reactors for various gas-liquid systems", Chem. Eng. Sci., 62(7), 7078-7089. https://doi.org/10.1016/j.ces.2007.07.071
  4. Chan, T.H.T., Yu, L., Tam, H.Y., Ni, Y.Q., Liu, S.Y., Chung, W.H. and Cheng, L.K. (2006), "Fiber Bragg grating sensors for structural health monitoring of Tsing Ma bridge: Background and experimental observation", Eng. Struct., 28(5), 648-659. https://doi.org/10.1016/j.engstruct.2005.09.018
  5. Dai, Y.B., Liu, Y.J., Leng, J.S., Deng, G., and Asundi, A. (2009), "A novel time-division multiplexing fiber Bragg grating sensor interrogator for structural health monitoring", Opt. Laser. Eng., 47(10), 1028-1033. https://doi.org/10.1016/j.optlaseng.2009.05.012
  6. Guo, Z.W. and Bai G.C. (2009), "Application of least squares support vector machine for regression to reliability analysis", Chinese J. Aeronautics, 22(2), 160-166. https://doi.org/10.1016/S1000-9361(08)60082-5
  7. Hoschke, N., Lewis, C.J., Price, D.C., Scott, D.A., Gerasimov, V. and Wang, P. (2008), A self-organizing sensing system for structural health monitoring of aerospace vehicles, Advanced Information and Knowledge Processing, Part II: 51-76.
  8. Herszberg, I., Li, H.C.H., Dharmawan, F., Mouritz, A.P., Nguyen, M. and Bayandor, J. (2005), "Damage assessment and monitoring of composite ship joints", Compos. Struct., 67(2), 205-216. https://doi.org/10.1016/j.compstruct.2004.09.017
  9. Hu, X.L., Liang, D.K., Zeng, J. and Lu, G. (2010), "A long period grating for simultaneous measurement of temperature and strain based on support vector regression", J. Intel. Mat. Syst. Str., 21(10), 955-959. https://doi.org/10.1177/1045389X10374163
  10. KapilS, B. (2010), "On Lagrangian support vector regression", Expert Syst. Appl., 37(12), 8784-8792. https://doi.org/10.1016/j.eswa.2010.06.028
  11. Kerrouche, A., Boyle, W.J.O., Sun, T. and Grattan, K.T.V. (2009), "Design and in-the-field performance evaluation of compact FBG sensor system for structural health monitoring applications", Sensor. Actuat. A - Phys., 151(2), 107-112. https://doi.org/10.1016/j.sna.2009.01.021
  12. Maaskant, R., Alavie, T., Measures, R.M., Tadros, G., Rizkalla, S.H. and Guha-Thakurta, A. (1997), "Fiber-optic Bragg grating sensors for bridge monitoring", Cement Concrete Comp., 19(1), 21-33. https://doi.org/10.1016/S0958-9465(96)00040-6
  13. Mieloszyk, M., Krawczuk, M., Zak, A. and Ostachowicz, W. (2010), "An adaptive wing for a small-aircraft application with a configuration of fiber Bragg grating sensors", Smart Mater. Struct., 19(8), 085009(12pp). https://doi.org/10.1088/0964-1726/19/8/085009
  14. Moyo, P., Brownjohn, J.M.W., Suresh, R. and Tjin, S.C. (2005), "Development of fiber Bragg grating sensors for monitoring civil infrastructure", Eng. Struct., 27(7), 1828-1834. https://doi.org/10.1016/j.engstruct.2005.04.023
  15. Peng, C.P., Wang, J.B. and Huang, K.Y. (2010), "Reliable fiber sensor system with star-ring-bus architecture", Sensors, 10(8), 4194-4205. https://doi.org/10.3390/s100504194
  16. Prokopenko, M., Wang, P., Foreman, M., Valencia, P., Price, D. and Poulton, G. (2005), "On connectivity of reconfigurable impact networks in ageless aerospace vehicles", Robot. Auton. Syst., 53(1), 36-58. https://doi.org/10.1016/j.robot.2005.06.003
  17. Scott, D.A., Price, D.C., Hoschke, N. and Richards, W.L. (2009), "Structural health monitoring of thermal protection systems", Mater. Forum., 33, 457-464.
  18. Vapnik, V.(1999), The nature of statistical learning theory, Springer, USA.
  19. Yang, Z., Gu, X.S., Liang, X.Y. and Ling, L.C. (2010), "Genetic algorithm-least squares support vector regression based predicting and optimizing model on carbon fiber composite integrated conductivity", Mater. Design, 31(3), 1042-1049. https://doi.org/10.1016/j.matdes.2009.09.057
  20. Yeh, C.H., Chow, C.W., Wang, C.H., Shih, F.Y., Wu, Y.F. and Chi, S. (2009), "A simple self-restored fiber Bragg grating (FBG)-based passive sensing ring network", Meas. Sci. Technol., 20(4), 043001 (5pp). https://doi.org/10.1088/0957-0233/20/4/043001
  21. Yeo, T.L., Sun, T., Grattan, K.T.V., Parry, D., Lade, R. and Powell, B.D. (2005), "Characterization of polymer-coated fiber Bragg grating sensor for relative humidity sensing", Sensor. Actuat. B - Chem., 110(1), 148-155. https://doi.org/10.1016/j.snb.2005.01.033
  22. Zhang, J., Sato, T. and Susumu, I. (2006), "Support vector regression for on-line health monitoring of large-scale structures", Struct. Saf., 28(4), 392-406. https://doi.org/10.1016/j.strusafe.2005.12.001

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