DETECTION OF MULTIPLE DEFECTS BASED ON STRUCTURAL HEALTH

MONITORING OF PIPELINE USING GUIDED WAVES TECHNIQUE

HATEM MOSTAFA ELWALWAL

A thesis submitted in

fulfilment of the requirement for the award of the

Doctor of Philosophy

Faculty of Mechanical and Manufacturing Engineering

Universiti Tun Hussein Onn Malaysia

APRIL 2018

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I would like to dedicate this thesis to

MY PARENTS

and

MY FAMILY

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ACKNOWLEDGEMENTS

In the name of Allah, the Most Gracious, the Most Merciful. All praises be to Allah,

the Supreme Creator, Nourisher and Sustainer of the universe. We beseech the

Almighty to bestow His choicest blessings on our illustrious Master, Prophet

Muhammad (peace be upon Him), on His pure family, His noble companions and all

believers in every era.

I am extremely grateful to my supervisor; Assoc. Prof. Dr. Shahruddin Bin Mahzan

for all the patience, guidance, advice, ideas, criticisms, encouragement and continuous

discussions, my deepest gratitude goes to him.

I also, would love to extend my thanks to Prof. Dr. Ahmed N Abdalla at (UMP), who

has encouraged me, until the end of my PhD journey. I am extremely grateful to all

the laboratory staff in the NTD group inspection at the Faculty of Electrical, TATI

University College, Kemaman, Terengganu, Malaysia. (Assoc. Prof. Dr. Ahmed N

Abdalla, Dr. Damuji and Mr. Khruden Ali).

I am also indebted to all my family members, especially my wife, my sons, and my

parents for their sacrifice, patience and understanding that have been very important

in making the possible.

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ABSTRACT

Monitoring and inspecting the health condition and state of the pipelines are significant

processes for an early detection of any leaking or damages for avoiding disasters.

Although most Non Destructive Test (NDT) techniques are able to detect and locate

damage during the maintenance intervals, interrupted services could result in high cost

and lots of time consumed. In addition, most NDTs are utilized to detect and locate

single damage such as axial crack, circular crack, or vertical crack only. Unfortunately,

these NDTs are unable to detect or localize multi-type of damages, simultaneously. In

this research, the proposed method utilizes the Structural Health Monitoring (SHM)

based on guided wave techniques for monitoring steel pipeline continuously in

detecting and locating multi-damages. These multi damages include the

circumference, hole and slopping cracks. A physical experimental works as well as

numerical simulation using ANSYS were conducted to achieve the research

objectives. The experimental work was performed to validate the numerical

simulation. An artificial neural network was used to classify the damages into ten

classes for each type of damage including circumference, hole and sloping cracks. The

obtained results showed that the numerical simulation was in agreement with the

experimental work with relative error of less than 1.5%. In addition, the neural network

demonstrated a feasible method for classifying the damages into classes with the

accuracy ranged from 75% to 82%. These results are important to provide substantial

information for active condition monitoring activities.

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ABSTRAK

Pemantauan dan pemeriksaan keadaan kesihatan dan tahap semasa saluran paip adalah

satu proses penting untuk pengesanan awal kebocoran atau kerosakan bagi

mengelakkan sebarang bencana. Walaupun kebanyakan teknik Ujian Tanpa Musnah

(NDT) dapat mengesan dan mencari kerosakan semasa proses penyelenggaraan

berkala, namun ianya melibatkan kos yang amat tinggi dan waktu yang panjang

disebabkan oleh pemberhentian pengoperasian. Di samping itu, kebanyakan NDT

hanya boleh digunakan untuk mengesan dan mencari kerosakan jenis tunggal seperti

retak paksi, retak membulat atau retakan tegak sahaja. Oleh itu teknik NDT ini tidak

dapat mengesan atau melokasikan pelbagai jenis kerosakan pada satu-satu masa.

Dalam kajian ini, kaedah yang dicadangkan menggunakan teknik Pemantauan

Kesihatan Struktur (SHM) berdasarkan teknik gelombang berpandu untuk memantau

talian paip keluli secara berterusan untuk mengesan dan mencari pelbagai kerosakan.

Kerosakan ini termasuk lubang lilitan, lubang dan keretakan. Kerja-kerja eksperimen

fizikal serta simulasi berangka menggunakan ANSYS telah dijalankan untuk mencapai

matlamat penyelidikan. Kerja-kerja eksperimen telah dijalankan untuk mengesahkan

simulasi berangka. Rangkaian neural tiruan digunakan untuk mengklasifikasikan

kerosakan kepada sepuluh kelas untuk setiap jenis kerosakan termasuk lilitan, lubang

dan keretakan bercerun. Hasil yang diperolehi menunjukkan bahawa keputusan

simulasi berangka telah mencapai hasil kerja eksperimen dengan ralat relatif kurang

dari 1.5%. Di samping itu juga, rangkaian neural telah menunjukkan bahawa kaedah

yang boleh dilaksanakan dalam mengklasifikasikan kerosakan ke dalam kelas dengan

ketepatannya di antara 75% hingga 82%. Keputusan ini penting bagi membekalkan

maklumat penting untuk aktiviti pemantauan keadaan aktif.

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TABLE OF CONTENTS

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF SYMBOLS AND ABBREVIATIONS xvi

LIST OF APPENDICES xix

CHAPTER 1 INTRODUCTION 1

1.1 Introduction 1

1.2 Background 2

1.2.1 Destructive Testing techniques (DT) 2

1.2.2 Non-destructive techniques 3

1.3 Problem statement 4

1.4 Research objectives 4

1.5 Scope of the research 5

1.6 Thesis structure 6

CHAPTER 2 LITERATURE REVIEW 6

2.1 Introduction 7

2.2 Importance of pipeline integrity 7

2.3 Means of applying an SHM system to structural 9

2.4 Inspection and monitoring techniques 11

2.5 Structural health monitoring: Concept and Approaches 12

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2.5.1 Structural health monitoring process 13

2.5.2 Structural health monitoring methodologies 15

2.6 Structural health monitoring applications 17

2.6.1 Aerospace structures applications 17

2.6.2 Civil structures applications 18

2.7 Damage identification level for SHM technology 19

2.8 Guided wave propagation 20

2.8.1 Introduction to Lamb-Wave of plate theory 23

2.8.2 Wave propagation in hollow cylinders 28

2.8.3 Governing Equations in Cylindrical Coordinates 31

2.9 Dispersion curves of cylindrical guided waves 40

2.10 Basic theory of piezoelectric effects and equations 41

2.11 Guided wave excitation and sensing 43

2.12 Response surface method 45

2.13 Neuro-Fuzzy systems 47

2.13.1 Types of Neuro-Fuzzy systems 47

2.13.2 Adaptive Neuro-Fuzzy inference systems

structure 50

2.14 Related work for SHM based on GW 52

2.14.1 Review of damage modeling 53

2.14.2 Review of guided waves for SHM sensing

systems 56

2.14.3 Review of guided wave based on pipeline 61

2.15 Summary 64

CHAPTER 3 RESEARCH METHODOLOGY 65

3.1 Introduction 65

3.2 Flowchart of methodology 66

3.3 Experimental work 67

3.3.1 Pipe features and structures 68

3.3.2 PZT actuators and sensors 69

3.3.3 Mode characteristics and frequency range 73

3.3.4 Signal preprocessing 76

3.3.5 Artificial damage 77

3.4 Finite element analysis simulation 78

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3.5 Comparison experimental with simulation for

validation 79

3.6 Data analysis and feature extraction 80

3.7 Response surface method 81

3.8 Artificial Neural Network (ANN) 83

3.8.1 The basic of nervous in ANN 83

3.8.2 Type of activation function 84

3.9 Neural network for damage prediction and

classification 84

3.9.1 Data collection 85

3.9.2 Create and configure the network 86

3.9.3 Initialize the weights and biases 86

3.9.4 Train the network 86

3.9.5 Validate the network 88

3.9.6 Use the network 90

3.10 Summary 90

CHAPTER 4 RESULTS AND DISCUSSION 92

4.1 Introduction 92

4.2 Results of experimental work 92

4.3 Results of finite element analysis simulation 95

4.4 Validation 102

4.5 Results of response surface 105

4.6 Damage classification using ANN and based on

confusion matrix 108

4.6.1 Circumference damages classification based

on confusion matrix 108

4.6.2 Hole damages classification based on

confusion matrix 110

4.6.3 Sloping damages classification based on

confusion matrix 113

4.7 ANN performance evaluation 115

4.8 Discussion of the results 119

4.9 Summary 122

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CHAPTER 5 CONCLUSION AND FUTURE RECOMMENDATIONS 123

5.1 Conclusion 123

5.2 Contributions 125

5.3 Recommendations 126

REFERENCES 127

APPENDIX A 139

APPENDIX B 142

APPENDIX C 147

APPENDIX D 151

APPENDIX E 152

APPENDIX F 187

LIST OF PUBLICATIONS 197

VITA 198

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LIST OF TABLES

2.1 Severe pipeline accidents (Parfomak 2011) 9

2.2 Damage identification levels (Farrar & Worden, 2007) 20

2.3 Survey of guided wave damage detection technique 53

2.4 Guided wave for structural health monitoring 57

2.5 Guided wave based on pipeline 61

3.1 Collected sample and tools with brief description 69

3.2 The specification of steel pipe parameters 69

3.3 Artificial damage description 79

3.4 Class of circumference damage 87

3.5 Class of Hole damage 87

3.6 Class of sloping damage 888

3.7 Sample of confusion matrix terms 89

4.1 Average of experimental work 102

4.2 Maximum peak of the excitation signal and maximum

peak of the reflected signal versus time for all samples 104

4.3 Comparison experimental and simulation distance with

actual distance 105

4.4 Classification accuracy rate and misclassification rate

for circumference 109

4.5 Classification accuracy rate and misclassification rate

for hole 112

4.6 Classification accuracy rate and misclassification rate

for sloping 114

4.7 Training and testing data 115

4.8 Results of accuracy rate for all cases 122

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LIST OF FIGURES

1.1 Actual damages that cause of pipelines incidents

(Association, 2014) 2

2.1 Actual rupture in the pipeline (NTSB, 2002) 8

2.2 Fire from rupture which lasted approximately 55-min.

The support structure of suspension bridge seen in lower

left corner of fireball is (26-m) tall (NTSB, 2003) 9

2.3 Structural health monitoring (SHM) process (Farrar &

Worden, 2007) 14

2.4 Structural health monitoring methodologies (Worden &

Dulieu-Barton, 2004) 15

2.5 Structural health monitoring applications (Nakamura,

2012) 17

2.6 Graphic descriptions for elastic waves in solid media

(Diamanti, 2005) 23

2.7 A thin plate of 2h in thickness (Su & Ye, 2009) 23

2.8 Wave speed dispersion curves for a steel plate, (a) Phase

velocity curve, (b) Group velocity curve (Anton, 2008) 27

2.9 Flow chart showing the types of guided waves in pipes

(Leonard, 2003) 28

2.10 A cylindrical waveguide with the cylindrical coordinate

system (r,θ,z) (Rose, 20014) 29

2.11 Longitudinal wave with symmetric propagation along

the Pipe (Su & Ye, 2009) 29

2.12 Torsional wave propagation along the pipe in the ɵ

direction (Su & Ye, 2009) 30

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2.13 Flexural wave showing nonsymmetrical propagation (Su

& Ye, 2009) 30

2.14 Mode conversion concept (Graff, 1991 ) 31

2.15 Sample dispersion curves of three-inch schedule 40 steel

pipe including all of the longitudinal and torsional

modes, including axisymmetric modes L(0,n)/T(0,n)

(n=1,2,3, …) and non-axisymmetric modes

L(m,n)/T(m,n) (m=1,2,3,…, n=1,2,3, …) (Rose, 2014) 40

2.16 Group velocity dispersion curve for a l6-in, schedule 40

(152-mm bore diameter, 7-mm wall thickness) steel pipe

(Alleyne et al., 1998) 41

2.17 The piezoelectric effects schematic, (a) Positive

piezoelectric effect-sensor, (b) Converse piezoelectric

effect-actuator (Yan et al., 2014) 42

2.18 Examples of various transducers: (a) PZE (Giurgiutiu,

2008); (b) comb transducer (Rose, 1998); (c) MFC

(Raghavan, 2007); (d) wedge transducers (Culshaw ,

1998); (e) FBG (Rajic, 2009); (f) EMAT transducer

(Wilcox et al., 2005); (g) air-coupled transduce 44

2.19 Schematics of PZE applications (Badcock, 2000): (a)

Pitch-catch sensing; (b) Pulse-echo sensing; (c)

Thickness sensing mode; (d) Impact/AE detection; (e)

PZE phased array; (f) E/M impedance method 45

2.20 Cooperative neuro-fuzzy systems (Kosko, 1992) 48

2.21 Concurrent neuro-fuzzy systems (Boyacioglu & Avci,

2010) 49

2.22 Tagaki-Sugeno hybrid neuro-fuzzy (Porwal et al., 2004) 49

2.23 The architecture of ANFIS with 2 inputs and a single

output (Jang et al., 1997) 52

3.1 Methodology flow chart 66

3.2 (a) Schematic diagrams of the experiment setup (b)

Photo of the experiment setup 67

3.3 The positioning of pipe on a test stand 69

3.4 (a) Geometry of PZT. (b) A photo of the PZT and glue 69

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3.5 Illustrated PZT actuator/sensor position 71

3.6 (a) A cross section for the distribution of the PZTs (b) A

photo for the locations of the PZTs 71

3.7 An illustration for the wave propagation through a

damaged pipe 73

3.8 Propagation of wave in free damage pipe 73

3.9 Dispersion curve for the pipe (a) Dispersion curve Group

Velocity of (L and T mode). (b) The dispersion curve

Phase Velocity of (L and T mode) 76

3.10 Artificial damage for samples 79

3.11 Steps of setting numerical factors (a) For hole type, (b)

For circumference and sloping types 83

3.12 Setting the number of inputs 83

3.13 The general structure of the ANN (Pedrycz, 1997) 84

3.14 Overall steps of extracted feature classification using

ANN 85

4.1 Obtained displacement-time curve for (a)Free damaged

pipe, (b) circumference, (c)sloping and,(d) hole damages 93

4.2 Model of pipe, (a) free damage, (b) circumference

damage, (c) sloping damage, (d) hole damage 96

4.3 Free damaged pipe. (a) Meshing pipe simulation. (b)

Guided wave propagation and (c) Displacement-time

curve 96

4.4 Circumference damaged pipe (a) Meshing pipe

simulation (b) Guided wave propagation for

circumference damage (c) Displacement-time curve 99

4.5 Sloping damaged pipe (a) Meshing pipe simulation (b)

Guided wave propagation for sloping damage (c)

Displacement-time curve 99

4.6 Hole damaged pipe. (a) Meshing pipe simulation (b)

Guided wave propagation for hole damage (c)

Displacement-time curve for hole 101

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4.7 Maximum peak of the excitation signal and maximum

peak of the reflected signal from end pipe versus time for

perfect pipe 103

4.8 Maximum peak of the excitation signal and maximum

peak of the reflected signal from circumference damage

versus time 103

4.9 Number of samples for circumference 106

4.10 Hole number of samples 106

4.11 (a) Displacement-time curve for sample no. 1 (run no. 1

in the figure), (b) FFT, (c) Extracted harmonics 107

4.12 Results of the training, validating, testing and all

confusion matrix for Circumference damages 109

4.13 Classification accuracy rate for circumference 110

4.14 Results of the training, validating, testing and all

confusion matrix for hole damages 111

4.15 Classification accuracy rate for hole 112

4.16 Results of the training, validating, testing and all

confusion matrix for sloping damages 113

4.17 Classification accuracy rate for sloping 114

4.18 ANFIS training 117

4.19 Percentage error of ANFIS models for training data:

(a)CT. (b) CN. (c) CS. (d) CL 118

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LIST OF SYMBOLS AND ABBREVIATIONS

Latin Script

A0 fundamental asymmetric Lamb wave mode

c speed of sound

cg group velocity

cE elastic stiffness constant matrix

cl longitudinal wave speed

cp phase velocity

ct transverse wave speed

d half the thickness of the plate

D matrix

D electric displacement vector

E Young’s Modulus

E electric field strength vector

e piezoelectric stress constant matrix

et transposed matrix

f linear frequency

F(m,n) flexural modes group

k wavenumber

L(m,n) longitudinal mode group

m circumferential order of a mode

n group order of a mode

S strain vector

S0 fundamental symmetric Lamb wave mode

T stress vector

T(m,n) torsional mode group

u displacement vector

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uq displacement in q direction

ur displacement in r direction

uz displacement in z direction

v Poisson’s Ratio of the material

y perpendicular to the wave propagation direction

Greek Script

dilatational scalar potential

Lam'e constants

Lam'e constants

density of the material

stress vector

driving (angular) frequency

x direction of wave propagation

vector potential

εS clamping dielectric constant matrix

Laplace operator

AE Acoustic Emission

AFC Active Fiber Composite

ANFIS Artificial Neural Fuzzy System

ANN Artificial Neural Network

BEM Boundary Element Method

CWT Continuous Wavelet Transform

DOE Design of Experiment

DSP Digital Signal Processing

I/M Impedance Method

EMAT Electromagnetic Acoustic Transducers

EMI Electromechanical Impedance

FBG Fiber Bragg Grating

FEA ANSYS Finite Element Software

FEM Finite Element Method

FFT Fast Fourier Transform

FOS Fiber Optic Sensors

GLW Guided Lamb Wave

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GPR Ground Penetrating Radar

GW Guided Waves

IIG Integrated Impedance and Guided Wave

IR Thermal/Infrared Testing

IWSHM International Workshop on Structural Health Monitoring

LISA Local Interaction Simulation Approach

LM Laser Testing Methods

LT Leak Testing

MFC Macro-Fiber Composite

MFL Magnetic Flux Leakage

MT Microwave Testing

NDE Nondestructive Evaluation

NDT Non-Destructive Testing

NME Normal Mode Expansion

NR Neutron Radiographic Testing

NTSB National Transportation Safety Board

PDF Probability Density Function

PT Liquid Penetrant Testing

PTM Precursor Transformation Method

PVDF Polyvinylidene Fluoride

PWAS piezoelectric wafer active sensors

PZT Piezoelectric Transducers

RAPID Real-time Active Pipeline Integrated Detection

RSM Response surface methodology

RT Radiographic Testing

SH Shear Horizontal

SHM Structural Health Monitoring

STFT Short Time Fourier Transforms

STMR Single Transmitter Multi-Receiver

VA Vibration Analysis

WT Wavelet Transforms

WTC Wavelet Transform Coefficient

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LIST OF APPENDICES

A Experimental Work Photos and Features of PZT 140

B Programming the Experimental Parts (Microcontroller) 143

C Dispersion Curve 148

D Excitation Signal 152

E Experimental and FE Simulation Results 153

F MATLAB Software 197

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CHAPTER 1

INTRODUCTION

1.1 Introduction

The economy of countries in the world is heavily dependent on an extensive network

of distribution and transmission pipelines to transport the countries’ energy sources

such as gas, oil and water. Petroleum in most developed countries is the largest source

of energy transported using pipes. The network of pipelines has several advantages

over other transportation methods such as trucks or trains due to effectiveness of cost,

installation, and distribution inside cities. According to (Factbook, 2012), about

2,175,000 miles (3,500,000 km) of the pipeline have been installed in more than 120

countries in the world. However, many factors affect the pipelines network including

corrosion, mechanical damages, and manufacturing defects. Environmental factors

such as soil conditions where the pipelines are installed may also cause corrosion.

Under extreme conditions, corrosion can impact the pipes’ integrity as early as 5 years

after installation (Kishawy & Gabbar, 2010). In addition, mechanical damages such as

dents, gouges and removed metals can also impact the integrity of the pipelines due to

the unsuitable handling of the pipes, unsuitable backfilling or running equipment too

close to the pipe before it is backfilled (Macdonald et al., 2007; Warman et al., 2006).

Moreover, several types of manufacturing defects could cause failure in pipelines’

operations such as a defect in the longitudinal seam of the pipe which is the most

common manufacturing defects. Figure 1.1 shows the statistics on what cause pipeline

damages. Therefore, reliability and integrity of pipelines are significant conditions that

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grab researchers’ attention for detecting and monitoring the degradation of the pipeline

systems by regularly assessing their conditions.

Figure 1.1: Actual damages that cause of pipelines incidents (Association, 2014)

1.2 Background

There are several and different methods (Carandente et al., 2012; Eybpoosh et al.,

2016, 2017; Liu et al., 2016; Løvstad & Cawley, 2011, 2012; Peter & Wang, 2013;

Wang et al., 2015; Wang et al., 2010) that have been proposed and designed for

inspecting, detecting, localizing and characterizing various types of damages.

Industries typically implement a combination of several different destructive and non-

destructive inspection techniques for inspecting and monitoring pipelines that are used

for transporting oil and natural gas. These techniques are commonly used to ensure the

integrity of pipelines. However, there are different factors that can be used to classify

the existing techniques (Su & Ye, 2009). In this section, the applied current techniques

are classified into destructive and non-destructive.

1.2.1 Destructive Testing techniques (DT)

Destructive testing (DT) is a technique in which the operation of the monitoring or

inspecting needs to stop the functionality of the pipelines or temporarily taken out of

operation. Most DT techniques are based on determining some mechanical properties,

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such as strength, toughness and hardness of the structure (Kang et al., 2011; Puust et

al., 2010). Hydrostatic test is an example of the most common method used as DT.

The test involves pressurizing the pipeline to a point greater than the maximum

operating pressure. This is followed by observing the pressure for several hours to

determine if there are any leaks. However, there are some drawbacks of using DT

because of the potential risks that may arise during the test. These include leakage or

rupture due to the high pressure. If this happens, the hazardous materials in the pipeline

must be replaced with water to prevent environmental damage which causes service

interruptions and water removal difficulties. Consequently, DT is considered as an

unsuitable method for monitoring and inspecting the pipelines used for transporting

natural gas and oil. In addition, DT is time consuming and need high cost.

1.2.2 Non-destructive techniques

Non-Destructive Testing and Evaluation (NDT/E) is the process of assessing the

structural integrity of a material or component without causing any physical damage

to the test object. Non-destructive techniques (NDT) have clear advantages over DT.

The non-destructive techniques with pipelines typically involve a damage detection

discipline commonly referred to as non-destructive evaluation (NDE). Generally, non-

destructive testing (NDT) is used by the industry for assessing pipeline integrity and

reliability (Shi & Miro, 2017; Varela et al., 2015).It is an acceptable practice to detect

dangerous defects before they cause catastrophic failure or interruption to production.

Recently, NDE conducted at regular scheduled intervals during the lifetime of

engineered structures and assets, is clearly too unwieldy to achieve automatic damage

identification when the structures and assets are in service (Qatu et al., 2016).

However, ND need to be performed at regular maintenance intervals which make ND

does not provide on-line monitoring and detection of failures as they happen. To

overcome the limitation of none on-line monitoring and detection of failures,

researchers proposed a more reliable, economical monitoring system involves a

damage detection process known as structural health monitoring (SHM) (Packo et al.,

2011; Yu et al., 2008). In other words, SHM is defined as ‘the nondestructive and

continuous monitoring characteristics using an array of sensors related to the fitness

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of an engineered component as it operates, so as to diagnose the onset of anomalous

structural behavior (Kim & Kwon, 2015; Yang, 2009).

1.3 Problem statement

Although there are several SHM techniques being utilized for monitoring cracks in

pipelines, there are some limitations whereby most existing detection and monitoring

techniques are based on non-destructive techniques in which the detection requires a

service interruption. In other words, this requires the pipeline system to be taken out

for cracks inspection. Consequently, this interruption involves a high cost and lots of

time. In addition, most existing techniques are implemented and used to detect specific

and single type of crack such as axial crack (Liu et al., 2017), circular crack (Wang et

al., 2015), or vertical crack only. Moreover, most existing techniques are able to detect

cracks during maintenance intervals (as a basic requirement or regular maintenance

intervals). Thus, these techniques could not provide continuous monitoring on the

conditions of the pipeline for detecting cracks. Therefore, extra efforts are needed for

an early detection of single or multiple damages in pipelines network using guided

waves (GW) methods.

1.4 Research objectives

The goal of this research is to integrate a robust and reliable SHM technology with the

GW-based Piezoelectric Transducers (PZT) sensor array system. The research

objectives were identified as follows:

1. To develop an ANSYS model of guided waves, in order to acquire preliminary

understanding of the guided waves in the pipeline and to obtain simulation data.

2. To assess and validate the Finite Element Analysis Simulation data with the

experimental work.

3. To evaluate an SHM-based technology on GW technique for continuous

monitoring steel pipeline and detect, locate, and characterize different types of

damage(s).

4. To classify multiple damages (type, size, number, and location) using Artificial

Neural Network (ANN).

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1.5 Scope of the research

To achieve the objectives of the study, this work direction was limited to the following

scope:

1. For oil supply, natural gas and industries pipelines network, a schedule 40 steel

pipe was used and prepared for testing purpose in various research topics and

damage detection in particular. It was difficult to obtain the pipe if it was

purchased in a small quantity for an experiment as a non-commercial sample,

were difficult to obtain and extremely expensive. In addition, the pipes’ weight

is quite heavy to suspend in the air with a plastic wire to prevent wave

reflection. Therefore, a lighter and cheaper pipe was chosen as an alternative

for the experiment. The selected material is a carbon steel pipeline (diameter

60mm, length 1000mm and 4mm thickness).

2. Three types of modes were generated when the waves were propagating along

a cylindrical structure, which were: the longitudinal L(0,m), torsional T(0,m)

and flexural F(n,m) wave modes. L (0,2) mode was propagated as if it was non-

dispersive over the frequency range. Therefore, Guided waves propagation L

(0,2) mode was used in hollow cylinders. In addition, this mode was not

capable to detect axial damage, and therefore the artificial and simulated

damages were circumference, holed, and sloped.

3. There are numerous elements to a pipeline system, including the main body of

the individual pipe segments, flanged and welded joints, valves, fittings, and

pumping stations. The waves suffered substantial dispersion and mode

conversion that made it hard to analyze from the pipeline system. Therefore,

the monitored area was the main body of the individual pipe segments only.

4. The simulations of perfect, cracks and holes pipes based on the ultrasonic

guided-waves were conducted by using the ANSYS Finite Element Analysis

Simulation software (FEA). The pipe structure with Guided waves simulated

the pipes with the same parameters and boundary conditions.

5. The Artificial Neural Network (ANN) has been used for a wide range of

applications such as diseases’ diagnosis, mining gigantic data, speech

recognition, image processing, pattern recognition, classification and

prediction as well as many other applications. The ANN model was used to

classify different damages through the use of Matlab software.

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1.6 Thesis structure

This thesis is divided into five chapters and organized as follows:

Chapter One: Overviews the problem background. This is where the problem

statement is introduced, and the objectives of this research are specified. In addition,

the chapter states the scope of the research.

Chapter Two: In this chapter, an overview of SHM concepts and approaches

and its applications are introduced. Moreover, it provides an overview about guided

wave propagation in details. In addition, characteristics of piezoelectric effects and

equations are presented. Finally, this chapter explains the artificial neural network.

Chapter Three: In this chapter, the research methodology is discussed and

explained, and the main phases of experimental and numerical work were described.

In addition, it provides the details of the experimental setup including overall pipeline

inspection system. Finally, the Finite Element Analysis Simulation is also discussed,

including finite element method and classification using artificial neural network.

Chapter Four: In this chapter, the results and finding of experimental work

are explained together with the results and finding of ANSYS. Then the validation of

the two results is presented and discussed. Finally, the classification of the damages

based on confusion matrix is illustrated and discussed.

Chapter Five: This chapter summary of the results and conclusions are given.

A brief discussion on the future recommendations is also presented. The conclusion,

contributions of this study and the future works as well as the limitation of this study

are presented.

CHAPTER 2

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