Machine learning algorithms for damage detection: Kernel-based approaches

Journal of Sound and Vibration

Volumn 363, Issue , 2016, Pages 584-599

  Santos, Adam ^a   Figueiredo, Eloi ^b   Silva, M F M ^a   Sales, C S ^a   Costa, J C W A ^a  

^a FEDERAL UNIVERSITY OF PARÁ   (Brazil)

^b UNIVERSIDADE LUSÓFONA DE HUMANIDADES E TECNOLOGIAS   (Portugal)

Author keywords

Damage detection; Environmental conditions; Kernel; Operational conditions; Structural health monitoring

Indexed keywords

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DATA DESCRIPTION; IMAGE SEGMENTATION; LEARNING ALGORITHMS; LEARNING SYSTEMS; PRINCIPAL COMPONENT ANALYSIS; STRUCTURAL HEALTH MONITORING;

ACCELERATION TIME SERIES; CLASSIFICATION PERFORMANCE; ENVIRONMENTAL CONDITIONS; KERNEL; KERNEL PRINCIPAL COMPONENT ANALYSES (KPCA); ONE-CLASS SUPPORT VECTOR MACHINE; OPERATIONAL CONDITIONS; SUPPORT VECTOR DATA DESCRIPTION;

DAMAGE DETECTION;

EID: 84949908086     PISSN: 0022460X     EISSN: 10958568     Source Type: Journal    
DOI: 10.1016/j.jsv.2015.11.008     Document Type: Article

References (51)

1. E. Figueiredo, I. Moldovan, and M.B. Marques Condition Assessment of Bridges: Past, Present, and Future - A Complementary Approach 2013 Universidade Católica Editora Portugal
2. B. Glisic, and D. Inaudi Fibre Optic Methods for Structural Health Monitoring 2007 John Wiley & Sons, Inc. Hoboken, NJ, United States
3. C.R. Farrar, and K. Worden An introduction to structural health monitoring Philosophical Transactions of the Royal Society: Mathematical, Physical & Engineering Sciences 365 1851 2007 303 315 10.1098/rsta.2006.1928
4. H. Sohn, and C.R. Farrar Damage diagnosis using time series analysis of vibration signals Smart Materials and Structures 10 3 2001 446 451 10.1088/0964-1726/10/3/304
5. K. Worden, and J.M. Dulieu-Barton An overview of intelligent fault detection in systems and structures Structural Health Monitoring 3 1 2004 85 98 10.1177/147592170404186
6. S.G. Mattson, and S.M. Pandit Statistical moments of autoregressive model residuals for damage localisation Mechanical Systems and Signal Processing 20 3 2006 627 645 10.1016/j.ymssp.2004.08.005
7. E. Figueiredo, M. Todd, C. Farrar, and E. Flynn Autoregressive modeling with state-space embedding vectors for damage detection under operational variability International Journal of Engineering Science 48 10 2010 822 834 10.1016/j.ijengsci.2010.05.005
8. E.J.F. Figueiredo, Damage Identification in Civil Engineering Infrastructure under Operational and Environmental Conditions, Doctor of Philosophy in Civil Engineering, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal, 2010.
9. K. Worden, and G. Manson The application of machine learning to structural health monitoring Philosophical Transactions of the Royal Society: Mathematical, Physical & Engineering Sciences 365 1851 2007 515 537 10.1098/rsta.2006.1938
10. K. Worden, C.R. Farrar, G. Manson, and G. Park The fundamental axioms of structural health monitoring Philosophical Transactions of the Royal Society: Mathematical, Physical & Engineering Sciences 463 2082 2007 1639 1664 10.1098/rspa.2007.1834
11. H. Sohn, K. Worden, and C.R. Farrar Statistical damage classification under changing environmental and operational conditions Journal of Intelligent Material Systems and Structures 13 9 2002 561 574 10.1106/104538902030904
12. K. Worden, and A.J. Lane Damage identification using support vector machines Smart Materials and Structures 10 3 2001 540 547 10.1088/0964-1726/10/3/317
13. L. Bornn, C.R. Farrar, G. Park, and K. Farinholt Structural health monitoring with autoregressive support vector machines Journal of Vibration and Acoustics 131 2 2009 10.1115/1.3025827 021004-1-9
14. Y. Kim, J.W. Chong, and K.H.C.J. Kim Wavelet-based AR-SVM for health monitoring of smart structures Smart Materials and Structures 22 1 2013 1 12 10.1088/0964-1726/22/1/015003
15. J.W. Chong, Y. Kim, and K.H. Chon Nonlinear multiclass support vector machine-based health monitoring system for buildings employing magnetorheological dampers Journal of Intelligent Material Systems and Structures 25 12 2014 1456 1468 10.1177/1045389X13507343
16. N.L. Khoa, B. Zhang, Y. Wang, F. Chen, and S. Mustapha Robust dimensionality reduction and damage detection approaches in structural health monitoring Structural Health Monitoring 13 4 2014 406 417 10.1177/1475921714532989
17. A. Mita, and H. Hagiwara Quantitative damage diagnosis of shear structures using support vector machine KSCE Journal of Civil Engineering 7 6 2003 683 689 10.1007/BF02829138
18. H.-X. He, and W. ming Yan Structural damage detection with wavelet support vector machine: introduction and applications Structural Control and Health Monitoring 14 1 2007 162 176 10.1002/stc.150
19. A. Cury, and C. Crémona Pattern recognition of structural behaviors based on learning algorithms and symbolic data concepts Structural Control and Health Monitoring 19 2 2012 161 186 10.1002/stc.412
20. H. HoThu, and A. Mita Damage detection method using support vector machine and first three natural frequencies for shear structures Open Journal of Civil Engineering 3 2 2013 104 112 10.4236/ojce.2013.32012
21. C.M. Wen, S.L. Hung, C.S. Huang, and J.C. Jan Unsupervised fuzzy neural networks for damage detection of structures Structural Control and Health Monitoring 14 1 2007 144 161 10.1002/stc.116
22. C.R. Farrar, and K. Worden Structural Health Monitoring: A Machine Learning Perspective 2013 John Wiley & Sons, Inc. Hoboken, NJ, United States
23. E. Figueiredo, G. Park, C.R. Farrar, K. Worden, and J. Figueiras Machine learning algorithms for damage detection under operational and environmental variability Structural Health Monitoring 10 6 2011 559 572 10.1177/1475921710388971
24. M.A. Torres-Arredondo, D.A. Tibaduiza, L.E. Mujica, J. Rodellar, and C.-P. Fritzen Data-driven multivariate algorithms for damage detection and identification: evaluation and comparison Structural Health Monitoring 13 1 2014 19 32 10.1177/1475921713498530
25. T. Nguyen, T.H. Chan, and D.P. Thambiratnam Controlled Monte Carlo data generation for statistical damage identification employing Mahalanobis squared distance Structural Health Monitoring 13 4 2014 461 472 10.1177/1475921714521270
26. S. Hakim, and H.A. Razak Modal parameters based structural damage detection using artificial neural networks - a review Smart Structures and Systems 14 2 2014 159 189 10.12989/sss.2014.14.2.159
27. T.-Y. Hsu, and C.-H. Loh Damage detection accommodating nonlinear environmental effects by nonlinear principal component analysis Structural Control and Health Monitoring 17 3 2010 338 354 10.1002/stc.320
28. J. Kullaa, Is temperature measurement essential in structural health monitoring?, Proceedings of the 4th International Workshop on Structural Health Monitoring: From Diagnostic & Prognostics to Structural Health Monitoring, DEStech Publications, Stanford, CA, USA, 1993, pp. 717-724.
29. K. Worden, G. Manson, and N.R.J. Fieller Damage detection using outlier analysis Journal of Sound and Vibration 229 3 2000 647 667 10.1006/jsvi.1999.2514
30. R. Ruotolo, and C. Surace Using SVD to detect damage in structures with different operational conditions Journal of Sound and Vibration 226 3 1999 425 439 10.1006/jsvi.1999.2305
31. H. Kantz, and T. Schreiber Nonlinear Time Series Analysis 2nd ed. 2003 Cambridge University Press Cambridge, United Kingdom
32. E. Figueiredo, J. Figueiras, G. Park, C.R. Farrar, and K. Worden Influence of the autoregressive model order on damage detection Computer-Aided Civil and Infrastructure Engineering 26 3 2011 225 238 10.1111/j.1467-8667.2010.00685.x
33. R. Yao, and S.N. Pakzad Autoregressive statistical pattern recognition algorithms for damage detection in civil structures Mechanical Systems and Signal Processing 31 2012 355 368 10.1016/j.ymssp.2012.02.014
34. G.E.P. Box, G.M. Jenkins, and G.C. Reinsel Time Series Analysis: Forecasting and Control 4th ed. 2008 John Wiley & Sons, Inc. Hoboken, NJ, United States
35. R. Kothamasu, J. Shi, S.H. Huang, and H.R. Leep Comparison of selected model evaluation criteria for maintenance applications Structural Health Monitoring 3 3 2004 213 224 10.1177/1475921704042696
36. B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory, ACM, Pittsburgh, PA, USA, 1992, pp. 144-152. http://dx.doi.org/10.1145/130385.130401
37. C. Cortes, and V. Vapnik Support-vector networks Machine Learning 20 3 1995 273 297 10.1023/A:1022627411411
38. C.-C. Chang, and C.-J. Lin LIBSVM: a library for support vector machines ACM Transactions on Intelligent Systems and Technology 2 3 2011 1 27 10.1145/1961189.1961199
39. S.S. Keerthi, and C.-J. Lin Asymptotic behaviors of support vector machines with gaussian kernel Neural Computation 15 7 2003 1667 1689 10.1162/089976603321891855
40. B. Schölkopf, J.C. Platt, J.C. Shawe-Taylor, A.J. Smola, and R.C. Williamson Estimating the support of a high-dimensional distribution Neural Computation 13 7 2001 1443 1471 10.1162/089976601750264965
41. B. Schölkopf, A.J. Smola, R.C. Williamson, and P.L. Bartlett New support vector algorithms Neural Computation 12 5 2000 1207 1245 10.1162/089976600300015565
42. D.M.J. Tax, and R.P.W. Duin Support vector data description Machine Learning 54 1 2004 45 66 10.1023/B:MACH.0000008084.60811.49
43. W.-C. Chang, C.-P. Lee, C.-J. Lin, A Revisit to Support Vector Data Description (SVDD), Technical Report, National Taiwan University, Taipei, Taiwan, 2013.
44. B. Schölkopf, A. Smola, and K.-R. Muller Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 10 5 1998 1299 1319 10.1162/089976698300017467
45. B. Schölkopf, and A.J. Smola Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond 2001 The MIT Press Cambridge, MA, United States
46. S. Mika, B. Schölkopf, A. Smola, K.-R. Müller, M. Scholz, G. Rätsch, Kernel PCA and de-noising in feature spaces, Proceedings of the 1998 Conference on Advances in Neural Information Processing Systems 11, MIT Press, Denver, CO, USA, 1999, pp. 536-542.
47. V. Franc, and V. Hlavac Greedy algorithm for a training set reduction in the kernel methods Computer Analysis of Images and Patterns 2003 Springer Groningen, Netherlands 426 433 10.1007/978-3-540-45179-2-53
48. E. Figueiredo, G. Park, J. Figueiras, C. Farrar, K. Worden, Structural Health Monitoring Algorithm Comparisons Using Standard Data Sets, Technical Report LA-14393, Los Alamos National Laboratory, Los Alamos, NM, United States, 2009.
49. C.R. Farrar, P.J. Cornwell, S.W. Doebling, M.B. Prime, Structural Health Monitoring Studies of the Alamosa Canyon and I-40 Bridges, Technical Report LA-13635-MS, Los Alamos National Laboratory, Los Alamos, NM, United States, 2000.
50. B. Peeters, and G.D. Roeck One-year monitoring of the Z24-Bridge: environmental effects versus damage events Earthquake Engineering & Structural Dynamics 30 2 2001 149 171 10.1002/1096-9845(200102)30:2(149::AID-EQE1)3.0.CO;2-Z
51. E. Figueiredo, E. Flynn, Three-Story Buildings Structure to Detect Nonlinear Effects, Technical Report. SHMTools, Los Alamos National Laboratory, Los Alamos, NM, United States (2009).