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Shock and Vibration
Volume 7, Issue 6, Pages 381-397

An Experimental Technique for Structural Diagnostic Based on Laser Vibrometry and Neural Networks

Paolo Castellini and Gian Marco Revel

Università degli Studi di Ancona – Dipartimento di Meccanica Via Brecce Bianche 60131 Ancona, Italy

Received 11 December 2000; Revised 11 December 2000

Copyright © 2000 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In recent years damage detection techniques based on vibration data have been largely investigated with promising results for many applications. In particular, several attempts have been made to determine which kind of data should be extracted for damage monitoring.

In this work Scanning Laser Doppler Vibrometry (SLDV) has been used to detect, localise and characterise defects in mechanical structures. After dedicated post-processing, a neural network has been employed to classify LDV data with the aim of automating the detection procedure.

In order to demonstrate the feasibility and applicability of the proposed technique, a simple case study (an aluminium plate) has been approached using both Finite Element simulations and experimental investigations. The proposed methodology was then applied for the detection of damages on real cases, as composite material panels. In addition, the versatility of the approach was demonstrated by analysing a Byzantine icon, which can be considered as a singular kind of composite structure.

The presented methodology has proved to be efficient to automatically recognise defects and also to determine their depth in composite materials. Furthermore, it is worth noting that the diagnostic procedure supplied correct results for the three investigated cases using the same neural network, which was trained with the samples generated by the Finite Element model of the aluminium plate. This represents an important result in order to simplify and shorten the procedure for the training set preparation, which often constitutes the main problem for the application of neural networks on real cases or in industrial environments.