Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network

Corrosion defect has inevitably causes serious incidents in pipeline structures. Reduction in corrosion related incidents are highly desirable due to safety and cost efficiency. Current approaches have implemented destructive testing which highly cost and time consumptions. Moreover, the techniques...

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Main Author:	Farag Elghanudi, Muheieddin Meftah
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	TA401-492 Materials of engineering and construction Mechanics of materials
Online Access:	http://eprints.uthm.edu.my/234/1/24p%20MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI.pdf http://eprints.uthm.edu.my/234/2/MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/234/3/MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI%20WATERMARK.pdf
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id	my-uthm-ep.234
record_format	uketd_dc
spelling	my-uthm-ep.2342021-07-13T03:15:03Z Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network 2018-07 Farag Elghanudi, Muheieddin Meftah TA401-492 Materials of engineering and construction. Mechanics of materials Corrosion defect has inevitably causes serious incidents in pipeline structures. Reduction in corrosion related incidents are highly desirable due to safety and cost efficiency. Current approaches have implemented destructive testing which highly cost and time consumptions. Moreover, the techniques were lacking in correlating corrosion behaviour and its damage severity. This research proposed several signal corrosion features extracted from time domain analysis which provide substantial information related to corrosion behaviour for damage classification analysis. Several corrosion damage scenarios were simulated with different depths indicating its severity conditions. Seven corrosion features in time domain were introduced and extracted from the strain signal obtained from multiple sensors attached to the pipeline structure. The aim was to obtain the monotonically linear behaviour in features which could provide good correlation between corrosion features and corrosion damage. The experimental features were validated with the computational simulation works done for undamaged case only representing the baseline conditions. These features were subsequently used as input parameters for artificial neural network to classify corrosion damage into six type of damage depth representing different damage severity. The results demonstrated only four corrosion features were found to have linear monotonically behaviour with impact damage which were maximum, minimum, peak to peak and standard deviation features. The simulation works obtained an average of 2 - 8% in relative error with the experimental results. The classification analysis also has demonstrated a feasible method for classifying damage into classes with the accuracy ranged from 84 – 98%. These findings were substantial in providing information for pipeline corrosion monitoring activities. 2018-07 Thesis http://eprints.uthm.edu.my/234/ http://eprints.uthm.edu.my/234/1/24p%20MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI.pdf text en public http://eprints.uthm.edu.my/234/2/MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/234/3/MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI%20WATERMARK.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Mekanikal dan Pembuatan
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TA401-492 Materials of engineering and construction Mechanics of materials
spellingShingle	TA401-492 Materials of engineering and construction Mechanics of materials Farag Elghanudi, Muheieddin Meftah Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network
description	Corrosion defect has inevitably causes serious incidents in pipeline structures. Reduction in corrosion related incidents are highly desirable due to safety and cost efficiency. Current approaches have implemented destructive testing which highly cost and time consumptions. Moreover, the techniques were lacking in correlating corrosion behaviour and its damage severity. This research proposed several signal corrosion features extracted from time domain analysis which provide substantial information related to corrosion behaviour for damage classification analysis. Several corrosion damage scenarios were simulated with different depths indicating its severity conditions. Seven corrosion features in time domain were introduced and extracted from the strain signal obtained from multiple sensors attached to the pipeline structure. The aim was to obtain the monotonically linear behaviour in features which could provide good correlation between corrosion features and corrosion damage. The experimental features were validated with the computational simulation works done for undamaged case only representing the baseline conditions. These features were subsequently used as input parameters for artificial neural network to classify corrosion damage into six type of damage depth representing different damage severity. The results demonstrated only four corrosion features were found to have linear monotonically behaviour with impact damage which were maximum, minimum, peak to peak and standard deviation features. The simulation works obtained an average of 2 - 8% in relative error with the experimental results. The classification analysis also has demonstrated a feasible method for classifying damage into classes with the accuracy ranged from 84 – 98%. These findings were substantial in providing information for pipeline corrosion monitoring activities.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Farag Elghanudi, Muheieddin Meftah
author_facet	Farag Elghanudi, Muheieddin Meftah
author_sort	Farag Elghanudi, Muheieddin Meftah
title	Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network
title_short	Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network
title_full	Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network
title_fullStr	Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network
title_full_unstemmed	Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network
title_sort	implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal dan Pembuatan
publishDate	2018
url	http://eprints.uthm.edu.my/234/1/24p%20MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI.pdf http://eprints.uthm.edu.my/234/2/MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/234/3/MUHEIEDDIN%20MEFTAH%20FARAG%20ELGHANUDI%20WATERMARK.pdf
_version_	1747830560728809472

Implementation of vibration-based structural health monitoring technique for identification of simulated corrosion damage in steel pipeline using neural network

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