Data-driven leakage diagnosis methods across pipeline and energy transportation system
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Future suggestion for pipeline leakages diagnosis.
view moreCredit: Dazhong Ma
A review recently published in the Journal of Pipeline Science and Engineering presents leakage diagnosis methods ranging from single pipelines to Energy Transmission Systems (ETS) — marking the first systematic attempt to connect single pipeline analysis with ETS. Its focus lies in summarizing leakage detection techniques under complex environmental conditions.
In leakage detection, despite theoretical advances, model-based methods face challenges in pipeline applications due to modeling inaccuracies and high computational costs. In contrast, data-driven approaches, especially deep learning models, show good potential by virtue of their strong capabilities in nonlinear mapping and spatiotemporal feature extraction, effectively addressing key ETS challenges such as highly coupled signals, background noise, and false alarms under multiple operating conditions.
Current research to improve detection in complex scenarios mainly proceeds in two directions: advanced signal processing and multi-modal fusion to enhance signal quality, SNR, and feature discriminability; and generative networks and transfer learning to solve few-shot or zero-shot learning problems for reliable detection with insufficient samples.
For leakage localization, the TDOA method remains fundamental due to its maturity, with research focusing on improving time-delay estimation via advanced signal processing and cross-correlation (CC) algorithms. To address weak signal attenuation in long-distance pipelines, novel localization methods based on attenuation model matching and adaptive dynamic programming (ADP) have been developed, redefining localization as a model parameter optimization problem. It paves a new avenue for the accurate localization of minor leakages in complex pipeline environments.
Notably, while data-driven methods have made significant progress, they still have limitations, including inherent constraints of pure data-driven models, weak self-learning ability, field deployment difficulties, and preventive maintenance issues.
Future research directions include data-physics fusion approaches for pipeline leakage diagnosis, self-learning pipeline leakage diagnosis method, large scale model-based leakage diagnostics, lightweight deployment of leakage detection models, locating multiple-point leakages in pipelines, pipeline leakage warning mechanism.
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Contact the author: Dazhong Ma, School of Information Science and Engineering, Northeastern University, Shenyang, China, madazhong@ise.neu.edu.cn
The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 200 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).
Journal
Journal of Pipeline Science and Engineering
Method of Research
Literature review
Subject of Research
Not applicable
Article Title
A review of data-driven leakage diagnosis methods across pipeline and energy transportation system
