Damage Detection in Steel Plates using Artificial Neural Networks

被引：0

作者：

Paulraj, M. P. ^{[1
]}

Majid, Mohd Shukry Abdul ^{[1
]}

Yaacob, Sazali ^{[1
]}

Rahim, Mohd Hafiz Faizulal ^{[1
]}

Krishnan, R. Pranesh ^{[1
]}

机构：

[1] Univ Malaysia Perlis, Sch Mechatron, Kangar 01000, Perlis, Malaysia

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, COMMUNICATION AND ENERGY CONSERVATION INCACEC 2009 VOL 1 | 2009年

关键词：

Time domain; Discrete Cosine Transformation; Damage Detection; Back Propagation neural network;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：292 / 295

页数：4

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