Chapter 1 defines the various terms and definitions related to structural health monitoring. The main technical approaches used for structural health monitoring are discussed. These include methods based on mathematical models of the undamaged and damaged structures and those based on modal data such as frequencies and mode shapes. In addition, localized damage detection methods based on strain monitoring and non-destructive testing are described. Three main soft computing methods (neural networks, genetic algorithms, and fuzzy logic) used for solving the structural health monitoring problem are discussed with reference to the published literature. The advantages and shortcomings of each of these methods are brought out, and techniques which hybridize these methods are shown to be good alternatives. The genetic fuzzy system is introduced as an excellent choice for solving the pattern recognition problems in structural health monitoring. The helicopter rotor system health and usage monitoring system is used as an example to illustrate the different ideas for a real engineering system. The chapter ends with a summary of the book.


Fuzzy Logic Damage Detection Structural Health Monitoring Lamb Wave General Regression Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Brian, D.: Larger helicopter HUM/FDR: benefits and developments. In: Proc. 55th Annual Forum of the American Helicopter Society, Montreal, Canada, pp. 1839–1846 (1999) Google Scholar
  2. 2.
    Cronkhite, J.: Practical application of health and usage monitoring (HUMS) to helicopter rotor, engine and drive system. In: Proc. 49th Annual Forum of the American Helicopter Society, St. Louis, MO, USA, pp. 1445–1455 (1993) Google Scholar
  3. 3.
    Land, J., Weitzman, C.: How HUMS have the potential of significantly reducing the direct operating costs for modern helicopters through monitoring. In: Proc. 51st Annual Forum of the American Helicopter Society, Fort Worth, TX, USA, pp. 744–757 (1995) Google Scholar
  4. 4.
    Pouradier, J., Trouvi, M.: An assessment of Eurocopter experience in HUMS development and support. In: Proc. 57th Annual Forum of the American Helicopter Society, Alexandria, VA, USA, pp. 1790–1797 (2001) Google Scholar
  5. 5.
    Stevens, P., Hall, D., Smith, E.: A multidisciplinary research approach to rotorcraft health and usage monitoring. In: Proc. 52nd Annual Forum of the American Helicopter Society, Washington, DC, USA, pp. 1732–1751 (1996) Google Scholar
  6. 6.
    Cleveland, G., Trammel, C.: Integrated health and usage monitoring system for the SH-60B helicopter. In: Proc. 52nd Annual Forum of the American Helicopter Society, Washington, DC, USA, pp. 1767–1787 (1996) Google Scholar
  7. 7.
    Kumar, S., Roy, N., Ganguli, R.: Monitoring low cycle fatigue damage in turbine blades using vibration characteristics. Mech. Syst. Signal Process. 21, 480–501 (2007) CrossRefGoogle Scholar
  8. 8.
    Roy, N., Ganguli, R.: Helicopter rotor blade frequency evolution with damage growth and signal processing. J. Sound Vib. 283, 821–851 (2005) CrossRefGoogle Scholar
  9. 9.
    Beena, P., Ganguli, R.: Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 11, 1014–1020 (2011) CrossRefGoogle Scholar
  10. 10.
    Gayathri, P., Umesh, K., Ganguli, R.: Effect of matrix cracking and material uncertainty on composite plates. Reliab. Eng. Syst. Saf. 95, 716–728 (2010) CrossRefGoogle Scholar
  11. 11.
    Ganguli, R., Chopra, I., Haas, D.: Detection of helicopter rotor system simulated faults using neural networks. J. Am. Helicopter Soc. 42, 161–171 (1997) CrossRefGoogle Scholar
  12. 12.
    Ganguli, R.: Health monitoring of helicopter rotor in forward flight using fuzzy logic. AIAA J. 40, 2773–2781 (2002) CrossRefGoogle Scholar
  13. 13.
    Miles, T., Lucas, M., Halliwell, N., Rothberg, S., et al.: Torsional and bending vibration measurements on rotors using laser technology. J. Sound Vib. 266, 441–467 (1999) CrossRefGoogle Scholar
  14. 14.
    Cabell, R., Fuller, C., O’Brien, W.: Neural network modelling of oscillatory loads and fatigue damage estimation of helicopter components. J. Sound Vib. 209, 329–342 (1998) CrossRefGoogle Scholar
  15. 15.
    Doebling, S., Farrar, C., Prime, M.: A summary review of vibration based damage identification methods. Shock Vib. Dig. 30, 91–105 (1998) CrossRefGoogle Scholar
  16. 16.
    Salawu, O.: Detection of structural damage through changes in frequency: a review. Eng. Struct. 19, 718–723 (1997) CrossRefGoogle Scholar
  17. 17.
    Diazvaldes, S., Soutis, C.: Delamination detection in composite laminates from variations of their modal characteristics. J. Sound Vib. 228, 1–9 (1999) CrossRefGoogle Scholar
  18. 18.
    Leonard, F., Lanteigne, J., Lalonde, S., Turcotte, Y., et al.: Free vibration behavior of a cracked beam and crack detection. Syst. Signal Process. 15, 529–48 (2001) CrossRefGoogle Scholar
  19. 19.
    Zou, Y., Tong, L., Steven, G.: Vibration based model dependant damage (delamination) identification and health monitoring for composite structures: a review. J. Sound Vib. 230, 357–78 (2000) CrossRefGoogle Scholar
  20. 20.
    Wahab, A.: Effect of modal curvatures on damage detection using model updating. Mech. Syst. Signal Process. 15, 439–45 (2001) CrossRefGoogle Scholar
  21. 21.
    Ratcliffe, C.: A frequency and curvature based experimental method for detecting damage in structures. J. Vib. Acoust. 122, 32–49 (2000) Google Scholar
  22. 22.
    Sampaio, R., Maia, N., Silva, J.: Damage detection using frequency response function curvature method. J. Sound Vib. 226, 1029–42 (1999) CrossRefGoogle Scholar
  23. 23.
    Hwang, K.C.: Damage detection in structures using a few frequency response measurements. J. Sound Vib. 2701, 1–14 (2004) CrossRefGoogle Scholar
  24. 24.
    Cattarius, J., Inman, D.: Experimental verification of intelligent fault detection in rotor blades. Int. J. Syst. Sci. 31, 1375–1379 (2000) MATHCrossRefGoogle Scholar
  25. 25.
    Schoess, J., Malver, F., Iyer, B., Kooyman, J., et al.: Rotor acoustic monitoring system (RAMS): a fatigue crack detection system. In: Proc. 53rd Annual Forum of the American Helicopter Society, Virginia Beach, VA, USA, pp. 274–281 (1997) Google Scholar
  26. 26.
    Lakshmanan, K., Pines, D.: Damage identification of chordwise crack size and location in uncoupled composite rotorcraft flexbeams. J. Intell. Mater. Syst. Struct. 9(2), 146–155 (1998) CrossRefGoogle Scholar
  27. 27.
    Purekar, A., Lakshmanan, K.: Detecting chordwise cracks and delamination in uncoupled composite rotorcraft flexbeams under rotation. In: Proc. 54th Annual Forum of the American Helicopter Society, Washington, DC, USA, pp. 1026–1043 (1998) Google Scholar
  28. 28.
    Oster, R.: Computed tomography as a non-destructive test method for fiber main rotor blades in development, series and maintenance. In: Computerized Tomography for Industrial Applications and Image Processing in Radiology, Berlin, Germany (1999). (Paper 4) Google Scholar
  29. 29.
    Giurgiutiu, V., Rogers, C.: Recent advancements in the electro-mechanical (E/M) impedance method for structural health monitoring and NDE. In: The SPIE’s 5th Annual International Symposium on Smart Structures and Materials, Catamaran Resort Hotel, CA (1998). (Paper 3329-53) Google Scholar
  30. 30.
    Ghoshal, A., Harrison, J., Sundaresan, M., Hughes, D., Schulz, M., et al.: Damage detection testing on a helicopter flexbeam. J. Intell. Mater. Syst. Struct. 12, 315–330 (2001) CrossRefGoogle Scholar
  31. 31.
    Diamanti, K., Soutis, C.: Structural health monitoring techniques for aircraft structures. Prog. Aerosp. Sci. 46, 342–352 (2010) CrossRefGoogle Scholar
  32. 32.
    Alkahe, J., Oshman, Y., Rand, O.: Adaptive estimation methodology for helicopter blade structural damage detection. J. Guid. Control Dyn. 25, 1049–1057 (2002) CrossRefGoogle Scholar
  33. 33.
    Gelsema, E.: Diagnostic reasoning based on a genetic algorithm operating in a Bayesian belief network. Pattern Recognit. Lett. 17, 1047–1055 (1996) CrossRefGoogle Scholar
  34. 34.
    Lucas, P.: Bayesian model-based diagnosis. Int. J. Approx. Reason. 27, 99–119 (2001) MATHCrossRefGoogle Scholar
  35. 35.
    Worden, K., Staszewski, W.J., Hensman, J.J.: Natural computing for mechanical systems research: a tutorial review. Mech. Syst. Signal Process. 25, 4–111 (2011) CrossRefGoogle Scholar
  36. 36.
    Simon, H.: Neural Networks and Learning Machines, 3rd edn. Pearson Education, Upper Saddle River (2009) Google Scholar
  37. 37.
    Kevin, G.: An Introduction to Neural Networks. Taylor and Francis, London (2003) Google Scholar
  38. 38.
    Laurene, V.F.: Fundamentals of Neural Networks: Architectures, Algorithms, and Applications. Prentice-Hall, Englewood Cliffs (1994) MATHGoogle Scholar
  39. 39.
    Mahapatra, D., Suresh, S., Omkar, S., Gopalakrishnan, S., et al.: Estimation of degraded laminate composite properties using acoustic wave propagation model and a reduction–prediction network. Eng. Comput. 22, 849–876 (2005) MATHCrossRefGoogle Scholar
  40. 40.
    Garg, A.K., Mahapatra, D., Suresh, S., Gopalakrishnan, S., Omkar, S.: Estimation of composite damage model parameters using spectral finite element and neural network. Compos. Sci. Technol. 64, 2477–2493 (2004) CrossRefGoogle Scholar
  41. 41.
    Chakraborty, D.: Artificial neural network based delamination prediction in laminated composites. Mater. Des. 26, 1–7 (2005) CrossRefGoogle Scholar
  42. 42.
    Kang, J., Choi, B., Lee, H., Kim, J., Kim, K., et al.: Neural network application in fatigue damage analysis under multiaxial random loadings. Int. J. Fatigue 28, 132–140 (2006) CrossRefGoogle Scholar
  43. 43.
    Al-Assaf, El-Kadi: Fatigue life prediction of unidirectional glass fiber/epoxy composite laminae using neural networks. Compos. Struct. 53, 65–71 (2001) CrossRefGoogle Scholar
  44. 44.
    Oberholster, A., Heyns, P.: On-line fan blade damage detection using neural networks. Mech. Syst. Signal Process. 20, 78–93 (2006) CrossRefGoogle Scholar
  45. 45.
    Yuan, S., Wang, L., Peng, G.: Neural network method based on a new damage signature for structural health monitoring. Thin-Walled Struct. 43, 553–563 (2005) CrossRefGoogle Scholar
  46. 46.
    Rao, M., Srinivas, J., Murthy, B.: Damage detection in vibrating bodies using genetic algorithms. Comput. Struct. 82, 963–968 (2004) CrossRefGoogle Scholar
  47. 47.
    Nag, A., Mahapatra, D., Gopalakrishnan, S.: Identification of delamination in composite beams using spectral estimation and a genetic algorithm. Smart Mater. Struct. 11, 899–908 (2002) CrossRefGoogle Scholar
  48. 48.
    Xu, Y., Liu, G., Wu, Z.: Damage detection for composite plates using lamb waves and projection genetic algorithm. AIAA J. 40, 1860–1866 (2002) CrossRefGoogle Scholar
  49. 49.
    Meruane, V., Heylen, W.: An hybrid real genetic algorithm to detect structural damage using modal properties. Mech. Syst. Signal Process. 25, 1559–1573 (2011) CrossRefGoogle Scholar
  50. 50.
    Sawyer, J., Rao, S.: Structural damage detection and identification using fuzzy logic. AIAA J. 38, 2328–35 (2000) CrossRefGoogle Scholar
  51. 51.
    Dempsey, P., Afjeh, A.: Integrated oil debris and vibration gear damage detection technologies using fuzzy logic. J. Am. Helicopter Soc. 49, 109–116 (2004) CrossRefGoogle Scholar
  52. 52.
    Soh, C., Bhalla, S.: Calibration of piezo-impedance transducers for strength prediction and damage assessment of concrete. Smart Mater. Struct. 14, 671–684 (2005) CrossRefGoogle Scholar
  53. 53.
    Zhao, Z., Chen, C.: A fuzzy system for concrete bridge damage diagnosis. Comput. Struct. 80, 629–641 (2002) CrossRefGoogle Scholar
  54. 54.
    Lopez, I., Sarigul-Klijn, N.: A review of uncertainty in flight vehicle structural damage, monitoring, diagnosis and control: challenges and opportunities. Prog. Aerosp. Sci. 46, 247–273 (2010) CrossRefGoogle Scholar
  55. 55.
    Cordon, O.: A historical review of evolutionary learning methods for Mamdani-type fuzzy rule based systems: designing interpretable genetic fuzzy systems. Int. J. Approx. Reason. 52, 894–913 (2011) CrossRefGoogle Scholar
  56. 56.
    Jiang, S.F., Zhang, C.M., Zhang, S.: Two-stage structural damage detection using fuzzy neural networks and data fusion. Expert Syst. Appl. 38, 511–519 (2011) CrossRefGoogle Scholar
  57. 57.
    Ramu, S., Johnson, V.: Damage assessment of composite structures using fuzzy logic integrated neural-network approach. Comput. Struct. 57, 491–502 (1995) MATHCrossRefGoogle Scholar
  58. 58.
    Wang, W., Ismail, F., Golnaraghi, F.: Neuro-fuzzy approach to gear system monitoring. IEEE Trans. Fuzzy Syst. 12, 710–723 (2004) CrossRefGoogle Scholar
  59. 59.
    Lee, E., Lam, H.: NN-based structural damage diagnosis using measured vibration data. In: Proceedings of the Knowledge-Based Intelligent Information and Engineering Systems. Part 3, vol. 3215, pp. 373–379 (2004) CrossRefGoogle Scholar
  60. 60.
    Zio, E., Gola, G.: A neuro-fuzzy technique for fault diagnosis and its application to rotating frequency. Reliab. Eng. Syst. Saf. 94, 78–88 (2009) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.College of EngineeringShri Vithal Education and Research InstitutePandharpurIndia
  2. 2.Department of Aerospace EngineeringIndian Institute of ScienceBangaloreIndia

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