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

Attention mechanism

Leakage localization

Long short-term memory

Pipeline fault diagnosis

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.

460-472

174

Zhang, Xinqi

Shi, Jihao

Yang, Ming

Huang, Xinyan

Usmani, Asif Sohail

Chen, Guoming

Fu, Jianmin

Huang, Jiawei

Li, Junjie

Process Safety and Environmental Protection

2023

journalArticle

0957-5820

10.1016/j.psep.2023.04.020

https://www.sciencedirect.com/science/article/pii/S0957582023003087