Identification of damage in bars using PZT sensors and regression techniques

  • K. T. Feroz
  • S. O. Oyadiji


A methodology is developed for the identification of defects in solid bars using longitudinal stress wave propagation data in conjunction with regression techniques. For the experimental study, a series of notches of different depths are cut at a fixed acial location of bars. Longitudinal waves are generated within the bars by means of the indirect collinear impact of a surface hardened spherical steel ball on one of the plane ends of the bars. In order to prevent local plastic deformation of the bar ends and to ensure that the waves induced in the bars are plane longitudinal waves, anvils are attached to the impacted ends of the bars. PZT (lead zirconium titanate) tiles of dimensions 5 X 3 mm, which are cut from standard PZT patches of dimensions 30 X 30 mm, are bonded on the surface of the bars and used as strain sensors. It is shown that the use of PZT sensors has several advantages over the use of conventional resistance strain gauges. A regression anaylysis technique is used to relate the strain histories from the notched bars to the strain history form the defect free bar. It is shown that the technique readily indicates the presence and size of a structural defect. In particular, it is shown that the area enclosed by the regression curve increases as the defect size increases.


Strain Sensor Lead Zirconium Titanate Strain History Wheatstone Bridge Electrical Resistance Strain 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abaqus User’s Manual, HKS(UK) Ltd., Vol. 1,2 and 3, 1992Google Scholar
  2. 2.
    Kalinke P and Weber T, A non destructive testing method to detect cracks in structures, International Symposium Non Destructive Testing in Civil Engineering (NDT-CE), 1995, pp l275 – 1281Google Scholar
  3. 3.
    Wendel R and Dual J, Application of neural networks to quantitative non-des tractive evaluation, Proceedings of the 16th ultrasonics international conference, Intelligent Signal Processing, UK, Ultrasonics 34, 1996, pp 461 – 465Google Scholar
  4. 4.
    Window A L and Holister G S, Strain Gauge Technology, Applied science publishers, 1982Google Scholar
  5. C Barton S, Volterra E G and Citron S J, On elastic impacts of spheres on long rods, Proc 3rd US Natl Cong Appi Mech, 1958, pp 89 – 94Google Scholar
  6. 6.
    Feroz K T and Oyadiji S 0, Defect Detection in rods using PZT sensors, Sensors and Actuators Conference, Manchester Business School, July 1996Google Scholar
  7. 7.
    Feroz K T, Impact dynamics of Rods, Beams and Panels, PhD The-sis, Manchester School of Engineering, University of Manchester, 1997Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • K. T. Feroz
    • 1
  • S. O. Oyadiji
    • 1
  1. 1.Division of Mechanical EngineeringManchester School of Engineering University of ManchesterManchesterUK

Personalised recommendations