Steel and Composite Structures

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

An intelligent health monitoring method for processing data collected from the sensor network of structure

  • Ghiasi, Ramin (Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan) ;
  • Ghasemi, Mohammad Reza (Department of Civil Engineering, Faculty of Engineering, University of Sistan and Baluchestan)
  • Received : 2018.02.11
  • Accepted : 2018.12.21
  • Published : 2018.12.25
⟨ Previous Next ⟩

Abstract

Rapid detection of damages in civil engineering structures, in order to assess their possible disorders and as a result produce competent decision making, are crucial to ensure their health and ultimately enhance the level of public safety. In traditional intelligent health monitoring methods, the features are manually extracted depending on prior knowledge and diagnostic expertise. Inspired by the idea of unsupervised feature learning that uses artificial intelligence techniques to learn features from raw data, a two-stage learning method is proposed here for intelligent health monitoring of civil engineering structures. In the first stage, $Nystr{\ddot{o}}m$ method is used for automatic feature extraction from structural vibration signals. In the second stage, Moving Kernel Principal Component Analysis (MKPCA) is employed to classify the health conditions based on the extracted features. In this paper, KPCA has been implemented in a new form as Moving KPCA for effectively segmenting large data and for determining the changes, as data are continuously collected. Numerical results revealed that the proposed health monitoring system has a satisfactory performance for detecting the damage scenarios of a three-story frame aluminum structure. Furthermore, the enhanced version of KPCA methods exhibited a significant improvement in sensitivity, accuracy, and effectiveness over conventional methods.

Keywords

References

  1. Alonso, L., Barbaran, J., Chen, J., Diaz, M., Llopis, L. and Rubio, B. (2018), "Middleware and communication technologies for structural health monitoring of critical infrastructures: A survey", Comput. Stand. Interf., 56(Supplement C), 83-100. https://doi.org/10.1016/j.csi.2017.09.007
  2. An, D., Kim, N.H. and Choi, J. (2015), "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews", Reliab. Eng. Syst. Saf., 133, 223-236. https://doi.org/10.1016/j.ress.2014.09.014
  3. Aronszajn, N. (1950), "Theory of reproducing kernels", Trans. Am. Math. Soc., 68(3), 337-404. https://doi.org/10.1090/S0002-9947-1950-0051437-7
  4. Berlinet, A. and Thomas-Agnan, C. (2011), Reproducing kernel Hilbert spaces in probability and statistics, Springer Science & Business Media.
  5. Boller, C., Chang, F.-K. and Fujino, Y. (2009), Encyclopedia of structural health monitoring, John Wiley & Sons.
  6. Cao, L.J., Chua, K.S., Chong, W.K., Lee, H.P. and Gu, Q.M. (2003), "A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine", Neurocomputing, 55, 321-336. https://doi.org/10.1016/S0925-2312(03)00433-8
  7. Cha, Y.-J. and Wang, Z. (2017), "Unsupervised novelty detectionbased structural damage localization using a density peaksbased fast clustering algorithm", Struct. Heal. Monit., 17(2), 313-324. https://doi.org/10.1177/1475921717691260
  8. Cha, Y., Choi, W. and Buyukozturk, O. (2017), "Deep learning‐based crack damage detection using convolutional neural networks", Comput. Civ. Infrastruct. Eng., 32(5), 361-378. https://doi.org/10.1111/mice.12263
  9. Chandorkar, M., Mall, R., Lauwers, O., Suykens, J.A.K. and De Moor, B. (2015), "Fixed-size least squares support vector machines: Scala implementation for large scale classification", 2015 IEEE Symposium Series on Computational Intelligence, pp. 522-528.
  10. De Boe, P. (2003), "Les elements piezo-lamines appliques a la dynamique des structures", Ph.D. Dissertation.
  11. De Brabanter, K., Karsmakers, P., Ojeda, F., Alzate, C., De Brabanter, J., Pelckmans, K., … Suykens, J.A.K. (2010), LSSVMlab Toolbox User's Guide: version 1.7. Katholieke Universiteit Leuven.
  12. Deraemaeker, A. and Worden, K. (2012), New trends in vibration based structural health monitoring (Vol. 520), Springer Science & Business Media.
  13. Farrar, C.R. and Worden, K. (2012), Structural health monitoring: a machine learning perspective, John Wiley & Sons.
  14. Farrar, C.R., Worden, K., Todd, M.D., Park, G., Nichols, J., Adams, D.E., Bement, M.T. and Farinholt, K. (2007), Nonlinear system identification for damage detection, Los Alamos National Laboratory (LANL), Los Alamos, NM, USA.
  15. Feeny, B.F. and Kappagantu, R. (1998), "On the physical interpretation of proper orthogonal modes in vibrations", J. Sound Vib., 211(4), 607-616. https://doi.org/10.1006/jsvi.1997.1386
  16. Figueiredo, E. and Flynn, E. (2009), "Three-story building structure to detect nonlinear effects", Rep. SHMTools Data Descr.
  17. Figueiredo, E., Park, G., Figueiras, J., Farrar, C. and Worden, K. (2009), Structural health monitoring algorithm comparisons using standard data sets, Los Alamos National Laboratory (LANL), Los Alamos, NM, USA.
  18. Figueiredo, E., Park, G., Farrar, C.R., Worden, K. and Figueiras, J. (2011), "Machine learning algorithms for damage detection under operational and environmental variability", Struct. Heal. Monit., 10(6), 559-572. https://doi.org/10.1177/1475921710388971
  19. Ghiasi, R., Torkzadeh, P. and Noori, M. (2016), "A machinelearning approach for structural damage detection using least square support vector machine based on a new combinational kernel function", Struct. Heal. Monit., 15(3), 302-316. https://doi.org/10.1177/1475921716639587
  20. Ghiasi, R., Ghasemi, M.R. and Sohrabi, M.R. (2017), "Structural Damage Detection using Frequency Response Function Index and Surrogate Model Based on Optimized Extreme Learning Machine Algorithm", J. Comput. Methods Eng., 36(1), 1-17. https://doi.org/10.18869/acadpub.jcme.36.1.1
  21. Golub, G.H. and Van Loan, C.F. (1996), Matrix computations. 1996, Johns Hopkins University Press, Balt, MD, USA, pp. 374-426.
  22. Gui, G., Pan, H., Lin, Z., Li, Y. and Yuan, Z. (2017), "Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection", KSCE J. Civ. Eng., 21(2), 523-534. https://doi.org/10.1007/s12205-017-1518-5
  23. He, Q., Kong, F. and Yan, R. (2007), "Subspace-based gearbox condition monitoring by kernel principal component analysis", Mech. Syst. Signal Process., 21(4), 1755-1772. https://doi.org/10.1016/j.ymssp.2006.07.014
  24. Jia, F., Lei, Y., Lin, J., Zhou, X. and Lu, N. (2016), "Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data", Mech. Syst. Signal Process., 72, 303-315.
  25. Krishnan, M., Bhowmik, B., Hazra, B. and Pakrashi, V. (2018), "Real time damage detection using recursive principal components and time varying auto-regressive modeling", Mech. Syst. Signal Process., 101, 549-574. https://doi.org/10.1016/j.ymssp.2017.08.037
  26. Krzanowski, W. (2000), Principles of multivariate analysis- a user's perspective, Oxford University Press.
  27. Langone, R., Reynders, E., Mehrkanoon, S. and Suykens, J.A.K. (2017), "Automated structural health monitoring based on adaptive kernel spectral clustering", Mech. Syst. Signal Process., 90, 64-78. https://doi.org/10.1016/j.ymssp.2016.12.002
  28. Lei, Y., Jia, F., Lin, J., Xing, S. and Ding, S.X. (2016), "An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data", IEEE Trans. Ind. Electron., 63(5), 3137-3147. https://doi.org/10.1109/TIE.2016.2519325
  29. Malekzadeh, M. and Catbas, F.N. (2016), "A Machine Learning Framework for Automated Functionality Monitoring of Movable Bridges", In: Dynamics of Civil Structures, 2, 57-63.
  30. Malekzadeh, M., Atia, G. and Catbas, F.N. (2015), "Performancebased structural health monitoring through an innovative hybrid data interpretation framework", J. Civ. Struct. Heal. Monit., 5(3), 287-305. https://doi.org/10.1007/s13349-015-0118-7
  31. Mercer, J. (1909), "Functions of positive and negative type, and their connection with the theory of integral equations", Philos. Trans. R. Soc. London. Ser. A, Contain. Pap. a Math. or Phys. Character, 209, 415-446. https://doi.org/10.1098/rsta.1909.0016
  32. Nguyen, V.H. and Golinval, J. (2010), "Fault detection based on Kernel Principal Component Analysis", Eng. Struct., 32(11), 3683-3691. https://doi.org/10.1016/j.engstruct.2010.08.012
  33. Nguyen, T., Chan, T.H.T. and Thambiratnam, D.P. (2014), "Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance", Struct. Heal. Monit., 13(4), 461-472. https://doi.org/10.1177/1475921714521270
  34. Nobahari, M., Ghasemi, M.R. and Shabakhty, N. (2017), "Truss structure damage identification using residual force vector and genetic algorithm", Steel Compos. Struct., Int. J., 25(4), 485-496.
  35. Nystrom, E.J. (1930), "Uber die praktische Auflosung von Integralgleichungen mit Anwendungen auf Randwertaufgaben", Acta Math., 54(1), 185-204. https://doi.org/10.1007/BF02547521
  36. Peeters, B. and De Roeck, G. (2001), "One-year monitoring of the Z 24-Bridge: environmental effects versus damage events", Earthq. Eng. Struct. Dyn., 30(2), 149-171. https://doi.org/10.1002/1096-9845(200102)30:2<149::AID-EQE1>3.0.CO;2-Z
  37. Perez-Rendon, A.F. and Robles, R. (2004), "The convolution theorem for the continuous wavelet tranform", Signal Processing, 84(1), 55-67. https://doi.org/10.1016/j.sigpro.2003.07.014
  38. Rosipal, R. and Trejo, L.J. (2001), "Kernel partial least squares regression in reproducing kernel hilbert space", J. Mach. Learn. Res., 2(Dec), 97-123.
  39. Santos, A., Figueiredo, E., Silva, M.F.M., Sales, C.S. and Costa, J.C.W.A.C.W.A. (2016), "Machine learning algorithms for damage detection: Kernel-based approaches", J. Sound Vib., 363, 584-599. https://doi.org/10.1016/j.jsv.2015.11.008
  40. Scholkopf, B., Smola, A. and Muller, K.-R. (1998), "Nonlinear component analysis as a kernel eigenvalue problem", Neural Comput., 10(5), 1299-1319. https://doi.org/10.1162/089976698300017467
  41. Scholkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Muller, K.-R., Ratsch, G. and Smola, A.J. (1999), "Input space versus feature space in kernel-based methods", IEEE Trans. Neural Networks, 10(5), 1000-1017. https://doi.org/10.1109/72.788641
  42. Sokolova, M., Japkowicz, N. and Szpakowicz, S. (2006), "Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation", In: Australian Conference on Artificial Intelligence, pp. 1015-1021.
  43. Van Overschee, P. and De Moor, B.L. (2012), Subspace identification for linear systems: Theory-Implementation-Applications, Springer Science & Business Media.
  44. Wang, Z. and Cha, Y.-J. (2017), "Unsupervised Novelty Detection Techniques for Structural Damage Localization: A Comparative Study", In: Model Validation and Uncertainty Quantification, 3, 125-132.
  45. Wang, Z. and Cha, Y. (2018), "Automated damage-sensitive feature extraction using unsupervised convolutional neural networks", In: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 10598, 105981J.
  46. Williams, C.K.I. and Seeger, M. (2001), "Using the Nystrom method to speed up kernel machines", In: Advances in Neural Information Processing Systems, pp. 682-688.
  47. Worden, K., Farrar, C.R., Manson, G. and Park, G. (2007), "The fundamental axioms of structural health monitoring", Proc. R. Soc. London A Math. Phys. Eng. Sci., 463(2082), 1639-1664. https://doi.org/10.1098/rspa.2007.1834
  48. Yan, A.-M., Kerschen, G., De Boe, P. and Golinval, J.-C. (2005), "Structural damage diagnosis under varying environmental conditions-part I: a linear analysis", Mech. Syst. Signal Process., 19(4), 847-864. https://doi.org/10.1016/j.ymssp.2004.12.002
  49. Yang, T., Li, Y.-F., Mahdavi, M., Jin, R. and Zhou, Z.-H. (2012), "Nystrom method vs random fourier features: A theoretical and empirical comparison", In: Advances in Neural Information Processing Systems, pp. 476-484.

Cited by

  1. Prediction of the Bending Strength of Boltless Steel Connections in Storage Pallet Racks: An Integrated Experimental-FEM-SVM Methodology vol.2020, pp.None, 2020, https://doi.org/10.1155/2020/5109204
  2. Field-testing and numerical simulation of vantage steel bridge vol.10, pp.3, 2018, https://doi.org/10.1007/s13349-020-00396-2