Leak detection and size identification in fluid pipelines using a novel vulnerability index and 1-D convolutional neural network

Abstract

This paper proposes a leak detection and size identification technique in fluid pipelines based on a new leak-sensitive feature called the vulnerability index (VI) and 1-D convolutional neural network (1D-CNN). The acoustic emission hit (AEH) features can differentiate between normal and leak operating conditions of the pipeline. However, the multiple sources of acoustic emission hits, such as fluid pressure on the joints, interference noises, flange vibrations, and leaks in the pipeline, make the features less sensitive toward leak size identification in the pipeline. To address this issue, acoustic emission hit features are first extracted from the acoustic emission (AE) signal using a sliding window with an adaptive threshold. Since the distribution of the acoustic emission hit features changes according to the pipeline working conditions, a newly developed multiscale Mann–Whitney test (MMU-Test) is applied to the acoustic emission hit features to obtain the new vulnerability index feature, which shows the pipeline's susceptibility to leak and changes according to the pipeline working conditions. Finally, the vulnerability index is provided as input to a 1-D-CNN for leak detection and size identification, whose experimental results show  a higher accuracy as compared to the reference state-of-the-art methods under variable fluid pressure conditions.

KEYWORDS:

• Pipelines
• acoustic emission
• leak detection
• 1-D convolutional neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Technology development Program (S3106236) funded by the Ministry of SMEs and Startups (MSS, Korea). This work was also funded by Ministry of Trade, Industry and Energy (MOTIE) and supported by Korea Evaluation Institute of Industrial Technology (KIET). [RS-2022-00142509, The development of simulation stage and digital twin for Land Based Test Site and hydrogen powered vessel with fuel cell] and by the National IT Industry Promotion Agency (NIPA), grant funded by the Korean government Ministry of Science and ICT (MSIT), Grant No. S1712-22-1001, for development of a smart mixed reality technology for improving the pipe installation and inspection processes in the offshore structure fabrication.