Real-time pipeline leak detection and localization using an attention-based LSTM approach

Real-time pipeline leak detection and localization using an attention-based LSTM approach

Zhang, Xinqi (China University of Petroleum (East China))
Shi, Jihao (China University of Petroleum (East China); The Hong Kong Polytechnic University)
Yang, M. (TU Delft Safety and Security Science)
Huang, Xinyan (The Hong Kong Polytechnic University)
Usmani, Asif Sohail (The Hong Kong Polytechnic University)
Chen, Guoming (China University of Petroleum (East China))
Fu, Jianmin (China University of Petroleum (East China))
Huang, Jiawei (China University of Petroleum (East China))
Li, Junjie (China University of Petroleum (East China))

2023

Long short-term memory (LSTM) has been widely applied to real-time automated natural gas leak detection and localization. However, LSTM approach could not provide the interpretation that this leak position is localized instead of other positions. This study proposes a leakage detection and localization approach by integrating the attention mechanism (AM) with the LSTM network. In this hybrid network, a fully-connected neural network behaving as AM is first applied to assign initial weights to time-series data. LSTM is then used to discover the complex correlation between the weighted data and leakage positions. A labor-scale pipeline leakage experiment of an urban natural gas distribution network is conducted to construct the benchmark dataset. A comparison between the proposed approach and the state-of-the-arts is also performed. The results demonstrate our proposed approach exhibits higher accuracy with AUC = 0.99. Our proposed approach assigns a higher attention weight to the sensor close to the leakage position, indicating the variation of data from the sensor has a significant influence on leakage localization. It corresponds that the closer to the leakage position, the larger variation of monitoring pressure after leakage, which enhances the detection results’ trustiness. This study provides a transparent and robust alternative for real-time automatic pipeline leak detection and localization, which contributes to constructing a digital twin of emergency management of urban pipeline leakage.

Attention mechanism
Leakage localization
Long short-term memory
Pipeline fault diagnosis

http://resolver.tudelft.nl/uuid:a64bd421-420c-45cb-9147-313b007006c2

https://doi.org/10.1016/j.psep.2023.04.020

2023-10-12

0957-5820

Process Safety and Environmental Protection, 174, 460-472

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Institutional Repository

journal article

© 2023 Xinqi Zhang, Jihao Shi, M. Yang, Xinyan Huang, Asif Sohail Usmani, Guoming Chen, Jianmin Fu, Jiawei Huang, Junjie Li