Structural Damage Detection Based on an Improved Edge-Detection Technique

To localize small damage from mode shapes, the polynomial annihilation edge detection method has been proposed and demonstrated its effectiveness on different types of structural components [7]. However, much computational effort involved in this approach lowers the damage detection speed. To alleviate this difficulty, in this paper, we improve the approach by first using the divided difference approach to identify the region(s) in which jump discontinuities are located, and then only applying the polynomial annihilation method to points in the identified region. In this way, the computational burden of this approach is significantly relieved, while the accuracy is still maintained. The improved approach has been validated by numerical simulations on a cable-stayed bridge model. This approach only requires post-damage mode shapes.

Detection and Localisation of Structural Damage Based on the Polynomial Annihilation Edge Detection: An Experimental Verification

The present paper describes an experimental validation of a new structural damage detection method based on the Polynomial Annihilation Edge Detection (PAED) technique. It is well known that concentrated damage such as a crack, causes a discontinuity in the rotations and consequently in the first derivatives of the mode shapes. On this basis, the PAED, a numerical method for detecting discontinuities in smooth piecewise functions and their derivatives, can be applied to the problem of damage detection and localisation in beam-like structures for which only post-damage mode shapes are available. As described in this paper, in order to verify this approach experimentally (a numerical assessment having already been documented in previous papers), vibration tests on a cantilever steel beam with a saw-cut have been performed and the Operational Deflection Shapes (ODS) determined. As the approach requires a reasonably high spatial resolution of the ODS, a scanning laser vibrometer, capable of acquiring data rapidly at a very large number of observation points, was used.