Structural health monitoring on pipeline system using unsupervised machine learning algorithm

Pipeline network system has been the most vital infrastructure for several needs ranging from residential, industrial, oil and gas, aerospace, automotive and many more. However, such system is also vulnerable to defects at some point during its lifespan. Therefore, a proper structural health monitor...

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Main Author:	Zamani, Muhammad Nazrif
Format:	Thesis
Language:	English
Published:	2021
Subjects:	TA Engineering (General) Civil engineering (General)
Online Access:	http://eprints.utm.my/id/eprint/102004/1/MuhammadNazrifZamaniMSKA2021.pdf
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id	my-utm-ep.102004
record_format	uketd_dc
spelling	my-utm-ep.1020042023-07-25T10:17:29Z Structural health monitoring on pipeline system using unsupervised machine learning algorithm 2021 Zamani, Muhammad Nazrif TA Engineering (General). Civil engineering (General) Pipeline network system has been the most vital infrastructure for several needs ranging from residential, industrial, oil and gas, aerospace, automotive and many more. However, such system is also vulnerable to defects at some point during its lifespan. Therefore, a proper structural health monitoring on the pipeline system is vital to ensure optimum safety of users and efficient transportation of liquid and gas. The objectives of this study are to investigate the natural frequency of the pipeline, establish a machine learning algorithm for pipeline damage detection and demonstrate numerically the applicability of machine learning in defect identification on a pipeline. A total of 4 single long pipes pinned at both ends are modelled using ABAQUS software whereas the K-mean algorithm is built via Google Colaboratory. The pipes are in the form of 2D wire with no loads applied. The pipes are categorized into 2 natures i.e healthy and corroded and are partitioned into 4 parts. The corrosion is induced on 3 out of the 4 pipes specifically at one of the portioned parts prior to undergoing frequency analysis to acquire mode shapes with their respective natural frequencies. As for the algorithm, 2 clusters, 0 and 1 are determined and labelled as healthy and corroded respectively. Multiple mode number ranging from 0 to 11 that represent the range of 4 distinct natural frequency data for 4 different pipes are fed into the algorithm and classified based on the pre-determined clusters. Based on the results obtained, the presence of corrosion on the pipe influences the deformation of the pipe by imposing slightly higher natural frequency in the range of 1.03% to 10.4% and 2 out of 4 pipes with damage locations at 1 and 3 provide identical natural frequency. The algorithm exhibits inaccurate damage detection as it manages to identify two damage locations at 1 and 3 when only one mode number is fed but eventually provides accurate damage detection for all 3 locations when more than one mode number. However, due to the identical natural frequency for location 1 and 3, the damage localization cannot be performed by the algorithm. As a conclusion, the competency of K-mean clustering in defect identification has achieved a satisfactory remark with the exception of damage localization. 2021 Thesis http://eprints.utm.my/id/eprint/102004/ http://eprints.utm.my/id/eprint/102004/1/MuhammadNazrifZamaniMSKA2021.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:145906 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Civil Engineering Faculty of Engineering - School of Civil Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TA Engineering (General) Civil engineering (General)
spellingShingle	TA Engineering (General) Civil engineering (General) Zamani, Muhammad Nazrif Structural health monitoring on pipeline system using unsupervised machine learning algorithm
description	Pipeline network system has been the most vital infrastructure for several needs ranging from residential, industrial, oil and gas, aerospace, automotive and many more. However, such system is also vulnerable to defects at some point during its lifespan. Therefore, a proper structural health monitoring on the pipeline system is vital to ensure optimum safety of users and efficient transportation of liquid and gas. The objectives of this study are to investigate the natural frequency of the pipeline, establish a machine learning algorithm for pipeline damage detection and demonstrate numerically the applicability of machine learning in defect identification on a pipeline. A total of 4 single long pipes pinned at both ends are modelled using ABAQUS software whereas the K-mean algorithm is built via Google Colaboratory. The pipes are in the form of 2D wire with no loads applied. The pipes are categorized into 2 natures i.e healthy and corroded and are partitioned into 4 parts. The corrosion is induced on 3 out of the 4 pipes specifically at one of the portioned parts prior to undergoing frequency analysis to acquire mode shapes with their respective natural frequencies. As for the algorithm, 2 clusters, 0 and 1 are determined and labelled as healthy and corroded respectively. Multiple mode number ranging from 0 to 11 that represent the range of 4 distinct natural frequency data for 4 different pipes are fed into the algorithm and classified based on the pre-determined clusters. Based on the results obtained, the presence of corrosion on the pipe influences the deformation of the pipe by imposing slightly higher natural frequency in the range of 1.03% to 10.4% and 2 out of 4 pipes with damage locations at 1 and 3 provide identical natural frequency. The algorithm exhibits inaccurate damage detection as it manages to identify two damage locations at 1 and 3 when only one mode number is fed but eventually provides accurate damage detection for all 3 locations when more than one mode number. However, due to the identical natural frequency for location 1 and 3, the damage localization cannot be performed by the algorithm. As a conclusion, the competency of K-mean clustering in defect identification has achieved a satisfactory remark with the exception of damage localization.
format	Thesis
qualification_level	Master's degree
author	Zamani, Muhammad Nazrif
author_facet	Zamani, Muhammad Nazrif
author_sort	Zamani, Muhammad Nazrif
title	Structural health monitoring on pipeline system using unsupervised machine learning algorithm
title_short	Structural health monitoring on pipeline system using unsupervised machine learning algorithm
title_full	Structural health monitoring on pipeline system using unsupervised machine learning algorithm
title_fullStr	Structural health monitoring on pipeline system using unsupervised machine learning algorithm
title_full_unstemmed	Structural health monitoring on pipeline system using unsupervised machine learning algorithm
title_sort	structural health monitoring on pipeline system using unsupervised machine learning algorithm
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Civil Engineering
granting_department	Faculty of Engineering - School of Civil Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/102004/1/MuhammadNazrifZamaniMSKA2021.pdf
_version_	1776100823069097984

Structural health monitoring on pipeline system using unsupervised machine learning algorithm

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