Damage detection in steel plates using artificial neural networks

Abstract

In this paper, a simple method for crack identification in steel plates based on frame energy based Discrete Cosine Transformation (DCT) is presented. A simple experimental procedure is also proposed to measure the vibration at different positions of the steel plate. The plate is excited by an impulse signal and made to vibrate. Energy based DCT features are then extracted from the vibration signals which are measured at different locations. A simple neural network model is developed, trained by Back Propagation (BP), to associate the frame energy based DCT features with the damage or undamaged locations of the steel plate. The effectiveness of the system is validated through simulation.