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Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning Zayat, Abdullah; Obeed, Mohanad; Chaaban, Anas

Abstract

In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. The proposed model transmits ultrasound signals through a pipe using a custom-designed array of piezoelectric transmitters and receivers. We propose to use the Zadoff–Chu sequence to modulate the input signals, then utilize its correlation properties to estimate the pipe channel response. The processed signal is then fed to a DNN that extracts the features and decides whether there is a diversion or not. The proposed technique demonstrates an average classification accuracy of 90.3% (when one sensor is used) and 99.6% (when two sensors are used) on 34 inch pipes. The technique can be readily generalized for pipes of different diameters and materials.

Item Metadata

Title
Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning
Creator
Zayat, Abdullah; Obeed, Mohanad; Chaaban, Anas
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2022-12-07
Description
In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is a diversion on a pipe or not. The proposed model transmits ultrasound signals through a pipe using a custom-designed array of piezoelectric transmitters and receivers. We propose to use the Zadoff–Chu sequence to modulate the input signals, then utilize its correlation properties to estimate the pipe channel response. The processed signal is then fed to a DNN that extracts the features and decides whether there is a diversion or not. The proposed technique demonstrates an average classification accuracy of 90.3% (when one sensor is used) and 99.6% (when two sensors are used) on 34 inch pipes. The technique can be readily generalized for pipes of different diameters and materials.
Subject
high-density polyethylene (HDPE); ultrasonic-guided waves (UGWs); Zadoff–Chu sequence; deep neural network (DNN); convolutional neural network (CNN); recurrent neural network (RNN); long-short term memory (LSTM); piezoelectric; structural health monitoring (SHM)
Genre
Article
Type
Text
Language
eng
Date Available
2025-06-12
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0449102
URI
http://hdl.handle.net/2429/91338
Affiliation
Engineering, School of (Okanagan)
Citation
Sensors 22 (24): 9586 (2022)
Publisher DOI
10.3390/s22249586
Peer Review Status
Reviewed
Scholarly Level
Faculty; Researcher
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

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CC BY 4.0