Skip to main content
SpringerLink
Log in

Structural Health Monitoring and Damage Identification

Handbook of Experimental Structural Dynamics

Abstract

Structural dynamics is fundamentally concerned with the design, operation, and understanding of physical structures. A significant concern in the management of these, often very high-value, assets, is their state of health. When a structure sustains damage, this can have an extremely negative effect on its availability, and this will have serious implications for profitability and also the safety of any human operators or occupants. It is therefore important to implement some means of monitoring structural health so that incipient damage can be detected and remedial actions can be taken before negative consequences occur. The pertinent damage identification methodology for engineering structures is Structural Health Monitoring (SHM). This chapter presents an overview of SHM, with particular reference to implementations based on monitoring structural vibrations and waves. The main philosophy under discussion here is data-based SHM, where diagnostics are based on the interpretation of measured data directly, without recourse to physics-based models. The main technologies for carrying out data-based SHM are statistical pattern recognition and machine learning, and the chapter gives some background on these methods and provides some case studies to illustrate their use. One of the main approaches to damage detection is novelty detection, where one develops a statistical model of measured features from the undamaged structure of interest, and monitors subsequent data to see if there are deviations from the model, indicative of damage. A serious problem with this approach is that it is prone to false alarms if there are benign changes to the data, like operational or environmental variations. Such benign changes – referred to here as confounding influences, need to be compensated for, if the SHM system is to be reliable and error-free (as far as possible). The chapter considers how confounding influences arise, and how they can be removed in the data-driven context by data normalization. Finally, the chapter concludes with some discussion of how physics-based models can still have a potentially useful role in data-driven SHM.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

References

  1. Farrar C, Worden K (2012) Structural health monitoring: a machine learning perspective. Wiley, Hoboken

    Google Scholar 

  2. Farrar CR, Worden K (2007) An introduction to structural health monitoring. R Soc Philos Trans Math Phys Eng Sci 365:303–315

    Google Scholar 

  3. Doebling S, Farrar C, Prime M, Shevitz D (1996) Damage identification and health monitoring of structural and mechanical systems from changes in their vibration characteristics: a literature review. Technical report. Los Alamos National Laboratories, Los Alamos

    Google Scholar 

  4. Wenzel H (2009) The character of SHM in civil engineering. In: Boiler C, Chang F-K, Fujino Y (eds) Encyclopedia of structural health monitoring. Wiley, Chichester, pp 2031–2037

    Google Scholar 

  5. Rytter A (1993) Vibrational based inspection of civil engineering structures. PhD thesis, Department of Building Technology and Structural Engineering, Aalborg University

    Google Scholar 

  6. Worden K, Dulieu-Barton J (2004) Damage identification in systems and structures. Int J Struct Health Monit 3:85–98

    Google Scholar 

  7. Randall R (2011) Vibration-based condition monitoring: industrial, aerospace and automotive applications. Wiley-Blackwell, Chichester

    Google Scholar 

  8. Farrar C, Doebling S, Nix D (2001) Vibration-based structural damage identification. Philos Trans R Soc A Math Phys Eng Sci 359(1778):131–149

    MATH  Google Scholar 

  9. Fassois S, Sakellariou J (2007) Time-series methods for fault detection and identification in vibrating structures. Philos Trans R Soc A Math Phys Eng Sci 365(1851):411–448

    MathSciNet  Google Scholar 

  10. Friswell M (2007) Damage identification using inverse methods. Philos Trans R Soc A Math Phys Eng Sci 365:393–410

    Google Scholar 

  11. Mal A, Banerjee S, Ricci F (2007) An automated damage identification technique based on vibration and wave propagation data. Philos Trans R Soc A Math Phys Eng Sci 365:479–491

    Google Scholar 

  12. Staszewski W, Robertson A (2007) Time-frequency and time-scale analyses for structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365:449–477

    MathSciNet  Google Scholar 

  13. Papatheou E, Manson G, Barthorpe R, Worden K (2010) The use of pseudo-faults for novelty detection in SHM. J Sound Vib 329:2349–2366

    Google Scholar 

  14. Papatheou E, Manson G, Barthorpe RJ, Worden K (2014) The use of pseudo-faults for damage location in SHM: an experimental investigation on a Piper Tomahawk aircraft wing. J Sound Vib 333:971–990

    Google Scholar 

  15. Hayton P, Utete S, King D, King S, Anuzis P, Tarassenko L (2007) Static and dynamic novelty detection methods for jet engine health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):493–514

    Google Scholar 

  16. Sohn H (2007) Effects of environmental and operational variability on structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365:539

    Google Scholar 

  17. Worden K, Manson G (2006) The application of machine learning to structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):515–537

    Google Scholar 

  18. Fuentes R (2017) On Bayesian networks for structural health and condition monitoring. PhD thesis, University of Sheffield

    Google Scholar 

  19. Worden K, Manson G (2007) The application of machine learning to structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365(1851):515–537

    Google Scholar 

  20. Farrar C, Worden K, Park G (2010) Complexity: a new axiom for structural health monitoring? In Proceedings of 5th European workshop on structural health monitoring, Sorrento, pp 882–888

    Google Scholar 

  21. Boiler C, Buderath M (feb 2007) Fatigue in aerostructures - where structural health monitoring can contribute to a complex subject. Philos Trans R Soc A Math Phys Eng Sci 365:561–587

    Google Scholar 

  22. Sohn H, Farrar CR, Hemez F, Czarnecki J (1996) A review of structural health monitoring literature 1996–2001, Technical report. Los Alamos National Laboratory, Los Alamos

    Google Scholar 

  23. Oppenheim AV, Schaffer RW (1975) Digital signal processing. Prentice-Hall, Englewood Cliffe

    Google Scholar 

  24. Figueiredo E, Park G, Figueiras J, Farrar CR, Worden K (2009) Structural health monitoring algorithm comparisons using standard data sets: LANL report LA-14393, Technical report. Los Alamos National Laboratory, Los Alamos

    Google Scholar 

  25. Sohn H, Farrar C, Hunter N, Worden K (2001) Structural health monitoring using statistical pattern recognition techniques. ASME J Dyn Meas Syst Control 123:706–711

    Google Scholar 

  26. Yang Z, Yu Z, Sun H (oct 2007) On the cross correlation function amplitude vector and its application to structural damage detection. Mech Syst Signal Process 21:2918–2932

    Google Scholar 

  27. Wang L, Yang Z, Waters T (nov 2010) Structural damage detection using cross correlation functions of vibration response. J Sound Vib 329:5070–5086

    Google Scholar 

  28. Ng CT, Veidt M (2009) A Lamb-wave-based technique for damage detection in composite laminates. Smart Mater Struct 18:074006

    Google Scholar 

  29. Michaels JE (2008) Detection, localization and characterization of damage in plates with an in situ array of spatially distributed ultrasonic sensors. Smart Mater Struct 17:035035

    Google Scholar 

  30. Michaels J, Michaels T (2005) Detection of structural damage from the local temporal coherence of diffuse ultrasonic signals. IEEE Trans Ultrason Ferroelectr Freq Control 52:1769–1782

    Google Scholar 

  31. Dongarra J, Sullivan F (2000) Guest editors introduction to the top 10 algorithms. Comput Sci Eng 2:22–23

    Google Scholar 

  32. Salawu O (1997) Detection of structural damage through changes in frequency: a review. Eng Struct 19:718–723

    Google Scholar 

  33. Sampaio R, Maia N, Silva J (1999) Damage detection using the frequency-response-function curvature method. J Sound Vib 226:1029–1042

    Google Scholar 

  34. Kessler SS, Spearing S, Atalla MJ, Cesnik CE, Soutis C (2002) Damage detection in composite materials using frequency response methods. Compos Part B 33:87–95

    Google Scholar 

  35. Owolabi G, Swamidas A, Seshadri R (2003) Crack detection in beams using changes in frequencies and amplitudes of frequency response functions. J Sound Vib 265:1–22

    Google Scholar 

  36. Maia N, Silva J, ALMAS E, Sampaio R (2003) Damage detection in structures: from mode shape to frequency response function methods. Mech Syst Signal Process 17:489–498

    Google Scholar 

  37. Peng Z, Chu F (2004) Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech Syst Signal Process 18:199–221

    Google Scholar 

  38. Taha MMR (2006) Wavelet transform for structural health monitoring: a compendium of uses and features. Struct Health Monit 5(3):267–295

    Google Scholar 

  39. Nair KK, Kiremidjian AS, Law KH (2006) Time series-based damage detection and localization algorithm with application to the ASCE benchmark structure. J Sound Vib 291:349–368

    Google Scholar 

  40. Krishnan Nair K, Kiremidjian AS (2007) Time series based structural damage detection algorithm using Gaussian mixtures modeling. J Dyn Syst Meas Control 129(3):285

    Google Scholar 

  41. Yao R, Pakzad SN (2012) Autoregressive statistical pattern recognition algorithms for damage detection in civil structures. Mech Syst Signal Process 31:355–368

    Google Scholar 

  42. Lu Y, Gao F (2005) A novel time-domain auto-regressive model for structural damage diagnosis. J Sound Vib 283(3–5):1031–1049

    Google Scholar 

  43. Shumway RH, Stoffer DS (2011) Time series analysis and its applications. Springer, New York

    MATH  Google Scholar 

  44. Chen S, Billings S a, Cowan CFN, Grant PM (1990) Practical identification of NARMAX models using radial basis functions. Int J Control 52(769892610):1327–1350

    MATH  Google Scholar 

  45. Billings S (2013) Nonlinear system identification NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley, Chichester

    MATH  Google Scholar 

  46. Bishop C (2006) Pattern recognition and machine learning. Springer, New York

    MATH  Google Scholar 

  47. Rasmussen C, Williams C (2006) Gaussian processes for machine learning, vol 38. The MIT Press, Cambridge, MA

    MATH  Google Scholar 

  48. Worden K, Manson G, Cross EJ (2014) On Gaussian process NARX models and their higher-order frequency response functions. In: Solving computationally expensive engineering problems. Springer, Cham, pp 315–335

    Google Scholar 

  49. Mottershead J, Friswell M (1993) Model updating in structural dynamics: a survey. J Sound Vib 167(2):347–375

    MATH  Google Scholar 

  50. Cawley P, Adams RD (1979) The location of defects in structures from measurements of natural frequencies. J Strain Anal Eng Des 14:49–57

    Google Scholar 

  51. Pandey A, Biswas M, Samman M (1991) Damage detection from changes in curvature mode shapes. J Sound Vib 145(2):321–332

    Google Scholar 

  52. Hearn G, Testa RB (1991) Modal analysis for damage detection in structures. J Struct Eng 117(10):3042–3063

    Google Scholar 

  53. Jaishi B, Ren W-X (2006) Damage detection by finite element model updating using modal flexibility residual. J Sound Vib 290(1–2):369–387

    Google Scholar 

  54. Peeters B, De Roeck G (1999) Reference-based stochastic subspace identification for output-only modal analysis. Mech Syst Signal Process 13(6):855–878

    Google Scholar 

  55. Kaiser J (1950) Untersuchungen über das auftreten von geräuschen heim Zugversuch. PhD thesis, Technical University of Munich (TUM)

    Google Scholar 

  56. Fuentes R, Howard TP, Marshall MB, Cross EJ, Dwyer-Joyce RS (2016) Observations on acoustic emissions from a line contact compressed into the plastic region. Proc Inst Mech Eng J Eng Tribol 230(11):1371–1376

    Google Scholar 

  57. Baram J, Rosen M (1979) Fatigue life prediction by distribution analysis of acoustic emission signals. Mater Sci Eng 41(1):25–30

    Google Scholar 

  58. Baxter MG, Pullin R, Holford KM, Evans SL (2007) Delta T source location for acoustic emission. Mech Syst Signal Process 21(3):1512–1520

    Google Scholar 

  59. Hensman J, Mills R, Pierce SG, Worden K, Eaton M (2010) Locating acoustic emission sources in complex structures using Gaussian processes. Mech Syst Signal Process 24(1):211–223

    Google Scholar 

  60. Kurz JH, Grosse CU, Reinhardt HW (2005) Strategies for reliable automatic onset time picking of acoustic emissions and of ultrasound signals in concrete. Ultrasonics 43(7):538–546

    Google Scholar 

  61. Rayleigh L (1884) On waves propagating along the plane surface of an elastic solid. Proc Lond Math Soc 17:4–11

    MathSciNet  MATH  Google Scholar 

  62. Lamb H (1917) On waves in an elastic plate. Proc R Soc Series A 93:93–114

    MATH  Google Scholar 

  63. Worden K (2001) Rayleigh and Lamb waves – basic principles. Strain 37:167–172

    Google Scholar 

  64. Croxford A, Wilcox P, Drinkwater B, Konstantinidis G (2007) Strategies for guided-wave structural health monitoring. Proc R Soc A Math Phys Eng Sci 463(2087):2961–2981

    Google Scholar 

  65. Cross E, Worden K (2011) Approaches to nonlinear cointegration with a view towards applications in SHM. In: Proceedings of 8th international conference on damage assessment – DAMAS 2011, Oxford, UK

    Google Scholar 

  66. Papatheou E, Dervilis N, Maguire A, Antoniadou I, Worden K (2015) A performance monitoring approach for the novel Lillgrund offshore wind farm. IEEE Trans Ind Electron 46(c):1–1

    Google Scholar 

  67. Zhang Y, Li J (2006) Wavelet-based vibration sensor data compression technique for civil infrastructure condition monitoring. J Comput Civ Eng 20:390–399

    Google Scholar 

  68. Staszewski W, Tomlinson G, Boiler C (2004) Health monitoring of aerospace structures, Smart Sensor Technologies and Signal Processing. Wiley, Hoboken

    Google Scholar 

  69. Candès E (2006) Compressive sampling. In: Proceedings of the international congress of mathematicians Madrid, August 22–30, 2006. European Mathematical Society Publishing House, Zurich, pp 1433–1452

    Google Scholar 

  70. Yuequan Bao Y, Beck JL, Li HH (2011) Compressive sampling for accelerometer signals in structural health monitoring. Struct Health Monit Int J 10:235–246

    Google Scholar 

  71. Mascareñas D, Cattaneo A, Theiler J, Farrar C (2013) Compressed sensing techniques for detecting damage in structures. Struct Health Monit Int J 12:325–338

    Google Scholar 

  72. Johnson WB, Lindenstrauss J (1984) Extensions of Lipschitz mappings into a Hilbert space. Contemp Math 26:189–206

    MathSciNet  MATH  Google Scholar 

  73. Tibshirani R (1996) Regression selection and shrinkage via the lasso. J R Stat Soc B 58(1):267–288

    MATH  Google Scholar 

  74. Fuentes R, Mineo C, Pierce SG, Worden K, Cross EJ (2019) A probabilistic compressive sensing framework with applications to ultrasound signal processing. Mech Syst Signal Process 117:383–402

    Google Scholar 

  75. Rao SS (2010) Mechanical vibrations, 5th edn. Prentice Hall, Upper Saddle River

    Google Scholar 

  76. Särkkä S (2013) Bayesian filtering and smoothing. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  77. Caravani P, Watson ML, Thomson WT (1977) Recursive least-squares time domain identification of structural parameters. J Appl Mech 44:135

    Google Scholar 

  78. Paleologu C, Benesty J, Ciochia S (2008) A robust variable forgetting factor recursive least-squares algorithm for system identification. IEEE Signal Proc Lett 15(3):597–600

    Google Scholar 

  79. Cooper J, Worden K (2000) On-line physical parameter estimation with adaptive forgetting factors. Mech Syst Signal Proc 14:705–730

    Google Scholar 

  80. Jazwinski AH (1970) Stochastic processes and filtering theory, vol 64. Academic, New York

    MATH  Google Scholar 

  81. Grewal MS, Andrews AP (2001) Kalman filtering: theory and practice. Wiley, Hoboken

    MATH  Google Scholar 

  82. Yang J, Lin S, Huang H, Zhou L (2006) An adaptive extended Kalman filter for structural damage identification. Struct Control Health Monit 13:849–867

    Google Scholar 

  83. Pan S, Yang J, Huang H (2007) An adaptive extended Kalman filter for structural damage identification II: unknown inputs. Struct Control Health Monit 14:497–521

    Google Scholar 

  84. Zhou L, Wu S, Yang JN (2008) Experimental study of an adaptive extended Kalman filter for structural damage identification. J Infrastruct Syst 14(1):42–51

    Google Scholar 

  85. Corigliano A, Mariani S (2004) Parameter identification in explicit structural dynamics: performance of the extended Kalman filter. Comput Methods Appl Mech Eng 193(36–38):3807–3835

    MATH  Google Scholar 

  86. Mariani S, Corigliano A (2005) Impact induced composite delamination: state and parameter identification via joint and dual extended Kalman filters. Comput Methods Appl Mech Eng 194(50–52):5242–5272

    MATH  Google Scholar 

  87. Julier SJ, Uhlmann JK, Durrant-Whyte HF (1995) A new approach for filtering nonlinear systems. In: American control conference, proceedings of the 1995, vol 3. IEEE, pp 1628–1632

    Google Scholar 

  88. Julier SJ, Uhlmann JK (1997) New extension of the Kalman filter to nonlinear systems. In: Signal processing, sensor fusion, and target recognition VI, vol 3068. International Society for Optics and Photonics, pp 182–194

    Google Scholar 

  89. Wan EA, Van Der Merwe R (2000) The unscented Kalman filter for nonlinear estimation. In: Adaptive systems for signal processing, communications, and control symposium 2000. AS-SPCC. The IEEE 2000. IEEE, pp 153–158

    Google Scholar 

  90. LaViola JJ (2003) A comparison of unscented and extended Kalman filtering for estimating quaternion motion. In: American control conference, 2003. Proceedings of the 2003, vol 3. IEEE, pp 2435–2440

    Google Scholar 

  91. St-Pierre M, 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. In: IEEE intelligent vehicles symposium. CiteSeerX, pp 831–835

    Google Scholar 

  92. Gustafsson F, Hendeby G (2012) Some relations between extended and unscented Kalman filters. IEEE Trans Signal Process 60(2):545–555

    MathSciNet  MATH  Google Scholar 

  93. Wu M, Smyth AW (2007) Application of the unscented Kalman filter for real-time nonlinear structural system identification. Struct Control Health Monitor 14(7):971–990

    Google Scholar 

  94. Chatzi EN, Smyth AW (2009) The unscented Kalman filter and particle filter methods for nonlinear structural system identification with non-collocated heterogeneous sensing. Struct Control Health Monitor 16(1):99–123

    Google Scholar 

  95. Eftekhar Azam S, Ghisi A, Mariani S (2012) Parallelized sigma-point Kalman filtering for structural dynamics. Comput Struct 92-93:193–205

    Google Scholar 

  96. Mariani S, Ghisi A (2007) Unscented Kalman filtering for nonlinear structural dynamics. Nonlinear Dyn 49(1–2):131–150

    MATH  Google Scholar 

  97. Xie Z, Feng J (2012) Real-time nonlinear structural system identification via iterated unscented Kalman filter. Mech Syst Signal Process 28:309–322

    Google Scholar 

  98. Doucet A, Johansen AM (2009) A tutorial on particle filtering and smoothing: fifteen years later. Handb Nonlin Filter 12(656–704):3

    MATH  Google Scholar 

  99. Crisan D, Doucet A (2002) A survey of convergence results on particle filtering methods for practitioners. IEEE Trans Signal Process 50(3):736–746

    MathSciNet  MATH  Google Scholar 

  100. Hu X-L, Schön T, Ljung L (2008) A basic convergence result for particle filtering. IEEE Trans Signal Process 56(4):1337–1348

    MathSciNet  MATH  Google Scholar 

  101. Gordon NJ, Salmond DJ, Smith AF (1993) Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proc F (Radar Signal Proc) 140(2):107–113

    Google Scholar 

  102. Pitt MK, Shephard N (1999) Filtering via simulation: auxiliary particle filters. J Am Stat Assoc 94(446):590–599

    MathSciNet  MATH  Google Scholar 

  103. Douc R, Cappé O (2005) Comparison of resampling schemes for particle filtering. In: Image and signal processing and analysis, 2005. ISPA 2005. Proceedings of the 4th international symposium on. IEEE, pp 64–69

    Google Scholar 

  104. Hoi JD, Schon TB, Gustafsson F (2006) On resampling algorithms for particle filters. In: Nonlinear statistical signal processing workshop, 2006 IEEE. IEEE, pp 79–82

    Google Scholar 

  105. Chatzi E, Smyth A (2013) Particle filter scheme with mutation for the estimation of time-invariant parameters in structural health monitoring applications. Struct Control Health Monit 20:1081–1095

    Google Scholar 

  106. Corbetta M, Sbarufatti C, Manes A, Giglio M (2013) Stochastic definition of state-space equation for particle filtering algorithms. Chem Eng Trans:1–6

    Google Scholar 

  107. Corbetta M, Sbarufatti C, Giglio M, Todd MD (2018) Optimization of nonlinear, non-Gaussian Bayesian filtering for diagnosis and prognosis of monotonic degradation processes. Mech Syst Signal Process 104:305–322

    Google Scholar 

  108. Sbarufatti C, Corbetta M, Giglio M, Cadini F (2017) Adaptive prognosis of lithium-ion batteries based on the combination of particle filters and radial basis function neural networks. J Power Sources 344:128–140

    Google Scholar 

  109. Xue S, Tang H, Xie Q (2009) Structural damage detection using auxiliary particle filtering method. Struct Health Monit 8(2):101–112

    Google Scholar 

  110. Ching J, Beck JL, Porter KA (2006) Bayesian state and parameter estimation of uncertain dynamical systems. Probabilist Eng Mech 21(1):81–96

    Google Scholar 

  111. Andrieu C, Doucet A, Holenstein R (2010) Particle Markov chain Monte Carlo methods. J R Stat Soc B Stat Methodol 72(3):269–342

    MathSciNet  MATH  Google Scholar 

  112. Rippengill S, Worden K, Holford KM, Pullin R (2003) Automatic classification of acoustic emission patterns. Strain 39:31–41

    Google Scholar 

  113. Manson G, Worden K, Holford K, Pullin R (2001) Visualisation and dimension reduction of acoustic emission data for damage detection. J Intell Mater Syst Struct 12:529–536

    Google Scholar 

  114. Farrar CR, Worden K (2012) Structural health monitoring: a machine learning perspective. Wiley, Hoboken

    Google Scholar 

  115. Bishop CM (2006) Pattern recognition and machine learning. Springer, Berlin

    MATH  Google Scholar 

  116. Worden K, Manson G, Fieller NR (2000) Damage detection using outlier analysis. J Sound Vib 229(3):647–667

    Google Scholar 

  117. Dervilis N, Cross E, Barthorpe R, Worden K (2014) Robust methods of inclusive outlier analysis for structural health monitoring. J Sound Vib 333(20):5181–5195

    Google Scholar 

  118. Hawkins DM (1980) Identification of outliers. Springer, Dordrecht

    MATH  Google Scholar 

  119. Rousseeuw PJ, Leroy AM (2005) Robust regression and outlier detection. Wiley, New York

    MATH  Google Scholar 

  120. Hubert M, Debruyne M (2010) Minimum covariance determinant. Wiley Interdiscip Rev Comput Stat 2(1):36–43

    Google Scholar 

  121. Van Aelst S, Rousseeuw P (2009) Minimum volume ellipsoid. Wiley Interdiscip Rev Comput Stat 1(1):71–82

    Google Scholar 

  122. Papatheou E, Dervilis N, Maguire AE, Campos C, Antoniadou I, Worden K (2017) Performance monitoring of a wind turbine using extreme function theory. Renew Energy 113:1490–1502

    Google Scholar 

  123. Moya MM, Koch MW, Hostetler LD (1993) One-class classifier networks for target recognition applications. Proc World Congr Neural Netw:359–367

    Google Scholar 

  124. Zimek A, Gaudet M, Campello RJ, Sander J (2013) Subsampling for efficient and effective unsupervised outlier detection ensembles. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 428–436

    Google Scholar 

  125. Rousseeuw PJ, Driessen KV (1999) A fast algorithm for the minimum covariance determinant estimator. Technometrics 41(3):212–223

    Google Scholar 

  126. Murphy KP (2012) Machine learning: a probabilistic perspective. MIT Press, Cambridge, MA

    MATH  Google Scholar 

  127. Gelman A, Carlin J, Rubin D, Vehtari A, Dunson D, Stern H (2013) Bayesian data analysis, 3rd edn. CRC Press, New York

    MATH  Google Scholar 

  128. Beyer K, Goldstein J, Ramakrishnan R, Shaft U (1999) When is “nearest neighbor” meaningful? In: Database theory – ICDT’99. Springer, Berlin/Heidelberg, pp 217–235

    Google Scholar 

  129. Manson G, Worden K, Allman D (2003) Experimental validation of a structural health monitoring methodology: part III. Damage location on an aircraft wing. J Sound Vib 259(2):365–385

    Google Scholar 

  130. Worden K, Manson G, Hilson G, Pierce S (2008) Genetic optimisation of a neural damage locator. J Sound Vib 309(3):529–544

    Google Scholar 

  131. Dervilis N (2013) A machine learning approach to structural health monitoring with a view towards wind turbines. PhD thesis, University of Sheffield

    Google Scholar 

  132. Bull L, Worden K, Manson G, Dervilis N (2018) Active learning for semi-supervised structural health monitoring. J Sound Vib 437:373–388

    Google Scholar 

  133. Rogers TJ, Worden K, Fuentes R, Dervilis N, Tygesen UT, Cross EJ (2019) A Bayesian non-parametric clustering approach for semi-supervised structural health monitoring. Mech Syst Signal Process 119:100–119

    Google Scholar 

  134. Farrar C, Sohn H, Worden K (2001) Data normalization: a key for structural health monitoring, technical report. Los Alamos National Laboratory, Los Alamos

    Google Scholar 

  135. Alampalli S (2000) Effects of testing, analysis, damage, and environment on modal parameters. Mech Syst Signal Process 14(1):63–74

    Google Scholar 

  136. Cornwell P, Farrar CR, Doebling SW, Sohn H (1999) Environmental variability of modal properties. Exp Tech 23(6):45–48

    Google Scholar 

  137. Peeters B, Roeck GD (2001) One-year monitoring of the Z24-bridge: environmental effects versus damage events. Earthq Eng Struct Dyn 30:149–171

    Google Scholar 

  138. Sohn H, Dzwonczyk M, Straser EG, Kiremidjian AS, Law KH, Meng T (1999) An experimental study of temperature effect on modal parameters of the Alamosa canyon bridge. Earthq Eng Struct Dyn 28(8):879–897

    Google Scholar 

  139. Moser P, Moaveni B (2011) Environmental effects on the identified natural frequencies of the Dowling hall footbridge. Mech Syst Signal Process 25(7):2336–2357

    Google Scholar 

  140. Xu Y, Chen B, Ng C, Wong K, Chan W (2010) Monitoring temperature effect on a long suspension bridge. Struct Control Health Monit 17(6):632–653

    Google Scholar 

  141. Cao Y, Yim J, Zhao Y, Wang ML (2011) Temperature effects on cable stayed bridge using health monitoring system: a case study. Struct Health Monit 10(5):523–537

    Google Scholar 

  142. de Battista N, Brownjohn JM, Tan HP, Koo K-Y (2015) Measuring and modelling the thermal performance of the Tamar suspension bridge using a wireless sensor network. Struct Infrastruct Eng 11(2):176–193

    Google Scholar 

  143. Mahmoud M, Abe M, Fujino Y (2001) Analysis of suspension bridge by ambient vibration measurement using time domain method and its application to health monitoring. In: Proceedings of the international modal analysis conference-IMAC, vol 1. Society for Experimental Mechanics, pp 504–510

    Google Scholar 

  144. Cross E, Koo K-Y, Brownjohn J, Worden K (2013) Long-term monitoring and data analysis of the Tamar bridge. Mech Syst Signal Process 35:16–34

    Google Scholar 

  145. Xia Y, Hao H, Zanardo G, Deeks A (2006) Long term vibration monitoring of an rc slab: temperature and humidity effect. Eng Struct 28(3):441–452

    Google Scholar 

  146. Kim C-Y, Jung D-S, Kim N-S, Kwon S-D, Feng MQ (2003) Effect of vehicle weight on natural frequencies of bridges measured from traffic-induced vibration. Earthq Eng Eng Vib 2(1):109–115

    Google Scholar 

  147. Lanata F, Schoefs F (2011) Multi-algorithm approach for identification of structural behavior of complex structures under cyclic environmental loading. Struct Health Monit 11:51–67

    Google Scholar 

  148. Sohn H, Dzwonczyk M, Straser E, Law K, Meng T, Kiremidjian A (1998) Adaptive modeling of environmental effects in modal parameters for damage detection in civil structures. In: Smart systems for bridges, structures, and highways, pp 127–138

    Google Scholar 

  149. Moaveni B, He X, Conte J, Fraser M, Elgamal A (2009) Uncertainty analysis of Voigt bridge modal parameters due to changing environmental condition. In: Proceedings of international conference on modal analysis (IMAC-XXVII)

    Google Scholar 

  150. Ni Y, Hua X, Fan K, Ko J (2005) Correlating modal properties with temperature using long-term monitoring data and support vector machine technique. Eng Struct 27(12):1762–1773

    Google Scholar 

  151. Hua X, Ni Y, Ko J, Wong K (2007) Modeling of temperature–frequency correlation using combined principal component analysis and support vector regression technique. J Comput Civ Eng 21:122

    Google Scholar 

  152. Ni Y, Zhou H, Ko J (2009) Generalization capability of neural network models for temperature-frequency correlation using monitoring data. J Struct Eng 135(10):1290–1300

    Google Scholar 

  153. Worden K, Sohn H, Farrar C (2002) Novelty detection in a changing environment: regression and interpolation approaches. J Sound Vib 258(4):741–761

    Google Scholar 

  154. Worden K, Cross E (2018) On switching response surface models, with applications to the structural health monitoring of bridges. Mech Syst Signal Process 98:139–156

    Google Scholar 

  155. Dervilis N, Shi H, Worden K, Cross E (2016) Exploring environmental and operational variations in SHM data using heteroscedastic Gaussian processes. In: Dynamics of civil structures, vol 2. Springer, New York, pp 145–153

    Google Scholar 

  156. Surace C, Worden K (1997) Some aspects of novelty detection methods. In: Proceedings of the third international conference on modern practice in stress and vibration analysis, Dublin

    Google Scholar 

  157. Cross E, Manson G, Worden K, Pierce S (2012) Features for damage detection with insensitivity to environmental and operational variations. Proc R Soci A rspa20120031

    Google Scholar 

  158. Kullaa J (2011) Distinguishing between sensor fault, structural damage, and environmental or operational effects in structural health monitoring. Mech Syst Signal Process 25(8):1–8

    Google Scholar 

  159. Manson G (2002) Identifying damage sensitive, environment insensitive features for damage detection. In: Proceedings of the IES conference

    Google Scholar 

  160. Yan A, Kerschen G, De Boe P, Golinval J (2005) Structural damage diagnosis under varying environmental conditions part I: a linear analysis. Mech Syst Signal Process 19(4):847–864

    Google Scholar 

  161. Kullaa J (2004) Structural health monitoring under variable environmental or operational conditions. In: Proceedings of the 2nd European workshop on structural health monitoring

    Google Scholar 

  162. Shi H (2018) On nonlinear cointegration methods for structural health monitoring. PhD thesis, University of Sheffield

    Google Scholar 

  163. Yan A-M, Kerschen G, Boe PD, Golinval J-C (2005) Structural damage diagnosis under varying environmental conditions part II: local PCA for non-linear cases. Mech Syst Signal Process 19:865–880

    Google Scholar 

  164. Sohn H, Worden K, Farrar C (2002) Statistical damage classification under changing environmental and operational conditions. J Intell Mater Syst Struct 13(9):561

    Google Scholar 

  165. Dervilis N, Choi M, Taylor S, Barthorpe R, Park G, Farrar C, Worden K (2014) On damage diagnosis for a wind turbine blade using pattern recognition. J Sound Vib 333(6):1833–1850

    Google Scholar 

  166. Shi H, Worden K, Cross E (2016) A nonlinear cointegration approach with applications to structural health monitoring. In: Journal of Physics: Conference Series, vol 744, IOP Publishing, p 012025

    Google Scholar 

  167. Shi H, Worden K, Cross E (2018) A regime-switching cointegration approach for removing environmental and operational variations in structural health monitoring. Mech Syst Signal Process 103:381–397

    Google Scholar 

  168. Rogers T, Worden K, Fuentes R, Dervilis N, Tygesen U, Cross E (2019) A Bayesian non-parametric clustering approach for semi-supervised structural health monitoring. Mech Syst Signal Process 119:100–119

    Google Scholar 

  169. Cross EJ, Worden K, Chen Q (2011) Cointegration: a novel approach for the removal of environmental trends in structural health monitoring data. Proc R Soc Lond A Math Phys Eng Sci 467:2712–2732

    MATH  Google Scholar 

  170. Koo K-Y, Brownjohn J, List D, Cole R (2013) Structural health monitoring of the Tamar suspension bridge. Struct Control Health Monit 20(4):609–625

    Google Scholar 

  171. Cross E (2012) On structural health monitoring in changing environmental and operational conditions. PhD thesis, University of Sheffield

    Google Scholar 

  172. Dickey D, Fuller W (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431

    MathSciNet  MATH  Google Scholar 

  173. Dickey D, Fuller W (1981) Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica 49(4):1057–1072

    MathSciNet  MATH  Google Scholar 

  174. Engle R, Granger C Cointegration and error correction: representation, estimation and testing. Econometrica 55(2):251–276

    Google Scholar 

  175. Johansen S (1988) Statistical analysis of cointegration vectors. J Econ Dyn Control 12(2–3):231–254

    MathSciNet  MATH  Google Scholar 

  176. Pierce SG, Dong F, Atherton K, Culshaw B, Worden K, Manson G, Monnier T, Guy P, Baboux J-C, Assad J et al (2001) Damage assessment in smart composite structures: the Damascos program. In: Smart structures and materials 2001: smart structures and integrated systems, vol 4327. International Society for Optics and Photonics, Bellingham, pp 223–234

    Google Scholar 

  177. Fan W, Qiao P (2011) Vibration-based damage identification methods: a review and comparative study. Struct Health Monit 10(1):83–111

    Google Scholar 

  178. Farrar C, Worden K (2013) Structural health monitoring: a machine learning perspective. Wiley, Chichester

    Google Scholar 

  179. Vagnoli M, Remenyte-Prescott R, Andrews J (2018) Railway bridge structural health monitoring and fault detection: state-of-the-art methods and future challenges. Struct Health Monit 17(4):971–1007

    Google Scholar 

  180. Friswell MI, Mottershead JE (2001) Inverse methods in structural health monitoring. Key Eng Mater 204–205:201–210

    Google Scholar 

  181. Barthorpe RJ (2011) On model- and data-based approaches to structural health monitoring. PhD thesis, University of Sheffield

    Google Scholar 

  182. Gardner P, Lord C, Barthorpe RJ (2018) A probabilistic framework for forward model-driven SHM. In: European Workshop on Structural Health Monitoring (EWHSM), pp 1–13

    Google Scholar 

  183. Gorl E, Link M (2003) Damage identification using changes of Eigen frequencies and mode shapes. Mech Syst Signal Process 17(1):103–110

    Google Scholar 

  184. Farrar CR, Lieven NA (2007) Damage prognosis: The future of structural health monitoring. Philos Trans R Soc A Math Phys Eng Sci 365:623–632

    Google Scholar 

  185. Friswell M, Mottershead J (1995) Finite element model updating in structural dynamics. Springer, New York

    Google Scholar 

  186. Friswell MI, Mottershead JE (2001) Physical understanding of structures by model updating. In: Proceedings of international conference on structural system identification, pp 81–96

    Google Scholar 

  187. Mottershead JE, Link M, Friswell MI (2011) The sensitivity method in finite element model updating: a tutorial. Mech Syst Signal Process 25(7):2275–2296

    Google Scholar 

  188. Jaishi B, Ren W-X (2005) Structural finite element model updating using ambient vibration test results. J Struct Eng 131(4):617–628

    Google Scholar 

  189. Simoen E, De Roeck G, Lombaert G (2015) Dealing with uncertainty in model updating for damage assessment: a review. Mech Syst Signal Process 56:123–149

    Google Scholar 

  190. Haag T, Carvajal González S, Hanss M (2012) Model validation and selection based on inverse fuzzy arithmetic. Mech Syst Signal Process 32:116–134

    Google Scholar 

  191. Rasmussen C, Ghahramani Z (2001) Occam’s razor. In: Advances in neural information processing systems 13: proceedings of the 2000 conference, p 294

    Google Scholar 

  192. Beck JL, Katafygiotis LS (1998) Updating models and their uncertainties. I: Bayesian statistical framework. J Eng Mech 124(4):455–461

    Google Scholar 

  193. Katafygiotis LS, Beck JL (1998) Updating models and their uncertainties. II: model identifiability. J Eng Mech 124(4):463–467

    Google Scholar 

  194. Friswell MI, Penny JET, Garvey SD (1997) Parameter subset selection in damage location. Inverse Probl Eng 5(3):189–215

    Google Scholar 

  195. Friswell MI, Mottershead JE, Ahmadian H (1998) Combining subset selection and parameter constraints in model updating. J Vib Acoust Trans ASME 120:854–859

    Google Scholar 

  196. Ko JM, Sun ZG, Ni YQ (2002) Multi-stage identification scheme for detecting damage in cable-stayed Kap Shui Mun bridge. Eng Struct 24(7):857–868

    Google Scholar 

  197. Lee JJ, Lee JW, Yi JH, Yun CB, Jung HY (2005) Neural networks-based damage detection for bridges considering errors in baseline finite element models. J Sound Vib 280(3–5):555–578

    Google Scholar 

  198. Satpal SB, Guha A, Banerjee S (2016) Damage identification in aluminum beams using support vector machine: numerical and experimental studies. Struct Control Health Monit 23:446–457

    Google Scholar 

  199. Hariri-Ardebili MA, Pourkamali-Anaraki F (2018) Support vector machine based reliability analysis of concrete dams. Soil Dyn Earthq Eng 104:276–295

    Google Scholar 

  200. Nagel JB, Sudret B (2016) A unified framework for multilevel uncertainty quantification in Bayesian inverse problems. Probabilistic Engineering Mechanics 43:68–84

    Google Scholar 

  201. Li C, Mahadevan S (2016) Role of calibration, validation, and relevance in multilevel uncertainty integration. Reliab Eng Syst Saf 148:32–43

    Google Scholar 

  202. Gardner P, Lord C, Barthorpe RJ (2018) A multi-level uncertainty integration strategy for forward model-driven SHM. In: Proceedings of the international conference on noise and vibration engineering, pp 3681–3692

    Google Scholar 

  203. Kennedy MC, O’Hagan A (2001) Bayesian calibration of computer models. J R Stat Soc B Stat Methodol 63(3):425–464

    MathSciNet  MATH  Google Scholar 

  204. Gardner P, Lord C, Barthorpe RJ (2017) Bayesian calibration and bias correction for forward model-driven SHM. In: Proceedings of the 11th international workshop on structural health monitoring, pp 2019–2027

    Google Scholar 

  205. Gardner P, Lord C, Barthorpe RJ (2018) Bayesian history matching for forward model-driven structural health monitoring. In: Proceedings of IMAC XXXVI

    Google Scholar 

  206. Fault shuts large hadron collider for two months. The Guardian 2017

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dynamics Research Group, Department of Mechanical Engineering, University of Sheffield, Sheffield, UK

    R. Fuentes, E.J. Cross, P.A. Gardner, L.A. Bull, T.J. Rogers, R.J. Barthorpe, H. Shi, N. Dervilis & K. Worden

  2. Engineering Institute, Los Alamos National Laboratory, Los Alamos, NM, USA

    C.R. Farrar

Authors
  1. R. Fuentes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. E.J. Cross
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P.A. Gardner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. L.A. Bull
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. T.J. Rogers
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. R.J. Barthorpe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. H. Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. N. Dervilis
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. C.R. Farrar
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. K. Worden
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K. Worden .

Editor information

Editors and Affiliations

  1. University of Cincinnati, Cincinnati, OH, USA

    R. Allemang

  2. Mechanical Engineering, University of Massachusetts Lowell, Lowell, MA, USA

    Peter Avitabile

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Society for Experimental Mechanics

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Fuentes, R. et al. (2020). Structural Health Monitoring and Damage Identification. In: Allemang, R., Avitabile, P. (eds) Handbook of Experimental Structural Dynamics. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6503-8_23-1

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-6503-8_23-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-6503-8

  • Online ISBN: 978-1-4939-6503-8

  • eBook Packages: Springer Reference EngineeringReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Chapter history

  1. Latest

    Structural Health Monitoring and Damage Identification
    Published:
    28 December 2021

    DOI: https://doi.org/10.1007/978-1-4939-6503-8_23-2

  2. Original

    Structural Health Monitoring and Damage Identification
    Published:
    18 May 2021

    DOI: https://doi.org/10.1007/978-1-4939-6503-8_23-1

Search

Navigation