Damage detection in steel plates using discrete cosine transformation techniques and artificial neural network
Date
2009-10-11Author
Paulraj, M.P.
Mohd Shukry, Abdul Majid
Sazali, Yaacob
Mohd Hafiz, Fazalul Rahiman
R Pranesh, Krishnan
Metadata
Show full item recordAbstract
In this paper, a simple method for crack identification in steel plates based on the Frame Energy based Discrete Cosine
Transformation [DCT] moments 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. Frame Energy based DCT
moment 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 moment features with
the damage or undamaged locations of the steel plate. The effectiveness of the system is validated through simulation.