@inbook{c46de8596a384f6da338c060c33fc68a,
title = "Smart Online Monitoring of Industrial Pipeline Defects",
abstract = "Acoustic Wave Reflection (AWR) approach seems to be the future avenue for long pipeline monitoring, typically for the oil and gas industries. There are several research studies are available on successful detection of defects using this AWR approach in the pipelines based on the laboratory scaled experiments. The method seems to be successfully applied to few industrial scale pipelines as well. The paper is proposing a smart online monitoring system using this AWR approach together with the modern instrumentation and Internet of Things (IoT) features to integrated wireless sensor node, input acoustic wave signal optimisation and then remote collection of the AWR signal to determine the pipe defect location using the piping layout with the geographical positioning system (GPS). The paper presents the proposed smart online monitoring system.",
keywords = "Acoustic Wave Reflection (AWR), Artificial Intelligence (AI), Defect detection, Pipeline monitoring",
author = "Jyoti Sinha and Kassandra Papadopoulou",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.",
year = "2022",
month = jan,
doi = "10.1007/978-3-030-93639-6_30",
language = "English",
isbn = "9783030936389",
series = "International Congress and Workshop on Industrial AI 2021",
pages = "352--359",
editor = "Ramin Karim and Alireza Ahmadi and Iman Soleimanmeigouni and Ravdeep Kour and Raj Rao",
booktitle = "Smart Online Monitoring of Industrial Pipeline Defects",
}
