Defect detection and characterisation by laser vibrometry and neural networks

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 is employed to classify LDV data with the aim of automating the detection procedure. 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. 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 analysing a Byzantine icon, which can be considered as a singular kind of composite structure.