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UBC Theses and Dissertations
Diversion detection and localization in small HDPE pipes using guided-wave ultrasound and deep learning techniques Zayat, Abdullah Ahmed
Abstract
This thesis presents a comprehensive study on the use of advanced techniques for detecting and accurately locating diversions in small-diameter High Density Polyethylene (HDPE) pipes. The integrity of pipelines is critical for the safe and efficient transportation of fluids and gas, and the early detection of defects and unauthorized diversions is crucial for preventing environmental damage and economic losses. A novel technique is proposed in this thesis, which is based on the transmission of a specifically designed signal through the pipe using a custom-designed array of ultrasonic piezoelectric transmitters and receivers. The goal of this technique is to generate an estimate of the pipe channel characteristics, which can then be used for defect detection and localization. The proposed technique combines ultrasonic guided waves, channel estimation, and deep learning to detect and localize defects in HDPE pipes. The first stage uses a supervised learning technique, a CNN-LSTM based architecture, for diversion detection. The second stage extends the first work by proposing an semi-supervised learning technique that incorporates a transformer-based autoencoder as an anomaly detection algorithm, which is utilized to detect diversions as anomalies. Finally, we propose a transformerbased encoder for diversion localization, which allows us to accurately locate the diversions with high accuracy and low mean absolute error. The results of our simulations and experiments demonstrate the superiority of the proposed method over the state-of-the-art techniques and its ability to detect diverisions and localize them with high accuracy. The proposed technique is cost-effective and reliable and has the potential to be applied to real-world systems.
Item Metadata
| Title |
Diversion detection and localization in small HDPE pipes using guided-wave ultrasound and deep learning techniques
|
| Creator | |
| Supervisor | |
| Publisher |
University of British Columbia
|
| Date Issued |
2023
|
| Description |
This thesis presents a comprehensive study on the use of advanced techniques
for detecting and accurately locating diversions in small-diameter
High Density Polyethylene (HDPE) pipes. The integrity of pipelines is critical
for the safe and efficient transportation of fluids and gas, and the early
detection of defects and unauthorized diversions is crucial for preventing
environmental damage and economic losses. A novel technique is proposed
in this thesis, which is based on the transmission of a specifically designed
signal through the pipe using a custom-designed array of ultrasonic piezoelectric
transmitters and receivers. The goal of this technique is to generate
an estimate of the pipe channel characteristics, which can then be used for
defect detection and localization.
The proposed technique combines ultrasonic guided waves, channel estimation,
and deep learning to detect and localize defects in HDPE pipes.
The first stage uses a supervised learning technique, a CNN-LSTM based
architecture, for diversion detection. The second stage extends the first
work by proposing an semi-supervised learning technique that incorporates a
transformer-based autoencoder as an anomaly detection algorithm, which is
utilized to detect diversions as anomalies. Finally, we propose a transformerbased
encoder for diversion localization, which allows us to accurately locate
the diversions with high accuracy and low mean absolute error. The results
of our simulations and experiments demonstrate the superiority of the proposed
method over the state-of-the-art techniques and its ability to detect
diverisions and localize them with high accuracy. The proposed technique is
cost-effective and reliable and has the potential to be applied to real-world
systems.
|
| Genre | |
| Type | |
| Language |
eng
|
| Date Available |
2023-04-24
|
| Provider |
Vancouver : University of British Columbia Library
|
| Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
|
| DOI |
10.14288/1.0431390
|
| URI | |
| Degree | |
| Program | |
| Affiliation | |
| Degree Grantor |
University of British Columbia
|
| Graduation Date |
2023-05
|
| Campus | |
| Scholarly Level |
Graduate
|
| Rights URI | |
| Aggregated Source Repository |
DSpace
|
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Rights
Attribution-NonCommercial-NoDerivatives 4.0 International