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|Title: ||Structural steel plate damage detection using DFT spectral energy and artificial neural network|
|Authors: ||Paulraj, Murugesa Pandiyan, Prof. Madya Dr.|
Mohd Shukri, Abdul Majid
Sazali, Yaacob, Prof. Dr.
Abdul Hamid, Adom, Prof. Madya Dr.
Krishnan, Pranesh R.
Discrete Fourier Transformation
Experimental modal analysis
Radial basis function network
|Issue Date: ||21-May-2010 |
|Publisher: ||Institute of Electrical and Electronics Engineers (IEEE)|
|Citation: ||p. 1-6|
|Series/Report no.: ||Proceedings of the 6th International Colloquium on Signal Processing and Its Applications (CSPA) 2010|
|Abstract: ||In this paper, simple methods for crack identification
in steel plates and their classification based on the frame based
frequency domain features is presented. Based upon the
boundary conditions and experimental modal analysis, two
simple experimental methods are designed to measure the
vibration at different positions of the steel plate. The plate is
excited by an impulse signal and made to vibrate. The
propagated vibration signals are then recorded. The signal is
transformed into frequency domain by computing the Discrete
Fourier Transformation (DFT). The frequency spectral bands
are identified and the spectral energy is extracted as features.
The condition of the steel plate namely healthy or faulty is
associated with the extracted features to form a final feature
vector. Two simple neural network models were developed,
trained using Backpropagation (BP) and Radial Basis Function
(RBF) algorithms. The results and the effectiveness of the system
are validated through simulation.|
|Description: ||Link to publisher's homepage at http://ieeexplore.ieee.org/|
|Appears in Collections:||Abdul Hamid Adom, Prof. Dr.|
Sazali Yaacob, Prof. Dr.
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