Staff View: Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks

Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks

Link to publisher's homepage at http://www.elsevier.com/

Saved in:

Bibliographic Details
Main Authors:	Pandiyan, Paulraj Murugesa , Prof. Dr., Sazali, Yaacob, Prof. Dr., Mohd Shukry, Abdul Majid, Dr., Mohd Nor Fakhzan, Mohd Kazim, Krishnan, Pranesh
Other Authors:	paul@unimap.edu.my
Format:	Article
Language:	English
Published:	Elsevier Ltd. 2014
Subjects:	Feed-forward neural network Frame energy based statistical features Non destructive testing Vibration signals
Online Access:	http://dspace.unimap.edu.my:80/dspace/handle/123456789/34878
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my.unimap-34878
record_format	dspace
spelling	my.unimap-348782014-05-29T09:39:57Z Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks Pandiyan, Paulraj Murugesa , Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shukry, Abdul Majid, Dr. Mohd Nor Fakhzan, Mohd Kazim Krishnan, Pranesh paul@unimap.edu.my s.yaacob@unimap.edu.my shukry@unimap.edu.my fakhzan@unimap.edu.my Feed-forward neural network Frame energy based statistical features Non destructive testing Vibration signals Link to publisher's homepage at http://www.elsevier.com/ This paper discusses about the detection of damages present in the steel plates using nondestructive vibration testing. A simple experimental model has been developed to hold the steel plate complying with the simply supported boundary condition. Vibration patterns from the steel structure are captured based on the impact testing using a simple protocol. The vibration signals in normal condition of the steel plate are recorded. The damages of size 512 μ m to 1852 μ m are simulated manually on the steel plate using drill bits. The vibration signals in the fault condition of the steel plate are collected. The captured vibration signals are preprocessed and time domain based feature extraction algorithms are developed to extract features from the vibration signals. The conditions of the steel plate namely healthy and faulty are associated with the extracted features to establish input output mapping. A feed-forward neural network is modeled to classify the condition. The neural network parameters are adjusted to train the network. The performance of the network is calculated using Falhman criterion. 2014-05-29T09:39:57Z 2014-05-29T09:39:57Z 2013 Article Procedia Engineering, vol. 53, 2013, pages 376-386 978-162748634-7 1877-7058 http://www.sciencedirect.com/science/article/pii/S1877705813001689 http://dspace.unimap.edu.my:80/dspace/handle/123456789/34878 en Elsevier Ltd.
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Feed-forward neural network Frame energy based statistical features Non destructive testing Vibration signals
spellingShingle	Feed-forward neural network Frame energy based statistical features Non destructive testing Vibration signals Pandiyan, Paulraj Murugesa , Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shukry, Abdul Majid, Dr. Mohd Nor Fakhzan, Mohd Kazim Krishnan, Pranesh Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
description	Link to publisher's homepage at http://www.elsevier.com/
author2	paul@unimap.edu.my
author_facet	paul@unimap.edu.my Pandiyan, Paulraj Murugesa , Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shukry, Abdul Majid, Dr. Mohd Nor Fakhzan, Mohd Kazim Krishnan, Pranesh
format	Article
author	Pandiyan, Paulraj Murugesa , Prof. Dr. Sazali, Yaacob, Prof. Dr. Mohd Shukry, Abdul Majid, Dr. Mohd Nor Fakhzan, Mohd Kazim Krishnan, Pranesh
author_sort	Pandiyan, Paulraj Murugesa , Prof. Dr.
title	Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
title_short	Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
title_full	Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
title_fullStr	Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
title_full_unstemmed	Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
title_sort	structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks
publisher	Elsevier Ltd.
publishDate	2014
url	http://dspace.unimap.edu.my:80/dspace/handle/123456789/34878
_version_	1643797642742857728
score	13.214268

Structural steel plate damage detection using non destructive testing, frame energy based statistical features and artificial neural networks

Similar Items