Abstract

This research proposes an artificial intelligence (AI) detection model using convolutional neural networks (CNN) to automatically detect gas leaks in a long-distance pipeline. The change of gap pressure is collected when leakage occurs in the pipeline, and thereby the feature of gas leakage is extracted for building the CNN model. The gas leak patterns in the long-distance pipeline are analyzed. A pipeline detection model based on AI technology for automatically monitoring the leaks is proposed by extracting the feature of gas leakage. This model is tested by collecting gas pressure data from an existing natural gas pipeline system starting from Mailiao to Taoyuan in Taiwan. The testing result shows that the reduced model of leak detection can be used to detect the leaks from the upstream and downstream pipelines, and the AI-based pipeline leak detection system can obtain a satisfactory result.

Details

Title
Developing and Implementing an AI-Based Leak Detection System in a Long-Distance Gas Pipeline
Author
Te-Kwei Wang; Yu-Hsun, Lin; Jian-Yuan, Shen
Pages
169-180
Section
Articles
Publication year
2022
Publication date
Jun 21, 2022
Publisher
Taiwan Association of Engineering and Technology Innovation
ISSN
24150436
e-ISSN
25182994
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2687714694
Copyright
© 2022. This work is published under https://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.