Statistical time energy based damage detection in steel plates using artificial neural networks

Abstract

In this paper, a simple method for crack identification in steel plates based on statistical time energy is presented. A simple experimental procedure is also proposed to measure the vibration at different positions of a steel plate. The plate is excited by an impulse signal and made to vibrate; statistical features are then extracted from the vibration signals which are measured at different locations. These features are then used to develop a neural network model. A simple neural network model trained by back propagation algorithm is then developed based on the statistical time energy features to classify the damage location in a steel plate. The effectiveness of the system is validated through simulation.