Damage Detection in Structures by Electrical Impedance and Optimization Technique

  • V. LopesJr.
  • H. H. Müller-Slany
  • F. Brunzel
  • D. J. Inman
Conference paper
Part of the Solid Mechanics and Its Applications book series (SMIA, volume 89)


The current interest in research regarding predictive maintenance is the result of several factors, involving economic reasons, recent developments in sensor and actuator technology and the elimination of dangerous failures involving risk of human life. Vibration-based methods have been investigated for a long time, and new approaches are continuously proposed. However, the interpretation of vibration signals to identify damage is not an easy and straightforward task. In this context, smart material technology has become an area of increasing interest, and the electrical impedance technique has been accepted as an effective method for structural health monitoring because its easy implementation and simple structural evaluation. In this paper a new methodology is proposed, which combines impedance technique and the application of a diagnostic model based on vibration measurements and generated by optimization procedures.


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6 References

  1. 1.
    Banks, H.T., Inman, D.J., Leo, D.J., and Wang, Y.: An Experimentally Validated Damage Detection Theory in Smart Structures, Journal of Sound and Vibrations, 191 (1996) 859–880.CrossRefGoogle Scholar
  2. 2.
    Sun, F.: Piezoelectric Active Sensor and Electric Impedance Approach for Structural Dynamic Measurement, Master Thesis, Virginia Polytechnic Institute and State University — CIMSS, 1996.Google Scholar
  3. 3.
    Park, G.: Assessing Structural Integrity using Mechatronic Impedance Transducers with Applications in Extreme Environments, Phd Thesis, Virginia Polytechnic Institute and State University — CDMSS, April/2000.Google Scholar
  4. 4.
    Lopes Jr., V., Park, G., Cudney, H.H., and Inman, D.J.: Structural Integrity Identification Based on Smart Material and Neural Network. XVIII IMAC — International Modal Analysis Conference, San Antonio, Texas, Feb/2000, pp. 510–515.Google Scholar
  5. 5.
    Müller-Slany, H.H.; Pereira, J.A.; Weber, H.I.; Brunzel, F.: Schadensdiagnose für elastomechanische Strukturen auf der Basis adaptierter Diagnosemodelle und FRF-Daten. VDI-Berichte 1466 (1999), pp. 323–340.Google Scholar
  6. 6.
    Müller-Slany, H.H.; Brunzel, F.: Generierung und Anwendung realitätsnaher, dynamisch hochgenauer Rechenmodelle in der modellgestützten Schadensdiagnose. VDI-Berichte 1550 (2000), pp. 631–650.Google Scholar
  7. 7.
    Müller-Slany, H.H. (1993) A Hierarchical Scalarization Strategy in Multicriteria Optimization Problems, in B. Brosowski (ed.), Multicriteria Decision, Verlag Peter Lang, Frankfurt am Main: pp. 69–79.Google Scholar
  8. 8.
    NAG Library Mark 17, NAG Ltd, Oxford, UK, 1997.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2001

Authors and Affiliations

  • V. LopesJr.
    • 1
  • H. H. Müller-Slany
    • 2
  • F. Brunzel
    • 2
  • D. J. Inman
    • 3
  1. 1.Department of Mechanical EngineeringUNESPIlha SolteiraBrazil
  2. 2.Institute of MechanicsUniversity of DuisburgDuisburgGermany
  3. 3.Center for Intelligent Material Systems and StructuresVirginia Polytechnic Institute and State UniversityBlacksburgUSA

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