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  4. Vol 9, Issue 4, 2025

Research on Pipeline Leak Signal Reconstruction and Multi-Aperture Classification Method Based on Multi-Task 1D U-Net

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DOI: 10.23977/acss.2025.090404 | Downloads: 2 | Views: 55

Author(s)

Xiao Yuxin 1, Zhao Cun 1, Dong Taiji 2, Liu Xu 1, Li Yangyang 1, Ma Zhenzong 1

Affiliation(s)

1 College of Electrical and information Engineering, Northeast Petroleum University, Daqing, Heilongjiang Province, China
2 School of Communication Engineering, Northeast Petroleum University, Qinhuangdao, China

Corresponding Author

Xiao Yuxin

ABSTRACT

Pipeline leak detection is a key technology to ensure the safe operation of oil-gas and industrial pipelines. However, actually collected monitoring signals are often affected by noise interference, data loss, and difficult feature extraction, which seriously restrict detection accuracy and reliability. To address these challenges, this paper proposes a multi-task 1D U-Net based pipeline leak detection model, realizing collaborative optimization of signal reconstruction, denoising, and aperture classification. The model adopts a shared encoder and task-specific decoders architecture, integrates channel and spatial attention mechanisms to focus on key features, and uses uncertainty learning to dynamically adjust task loss weights, improving training stability and reducing manual tuning costs. Experimental verification on a 4,778-sample multi-channel dataset (each sample is a 5-channel,720-length time-series signal) shows significant performance: denoising task MSE drops to 0.0013(training set) and 0.0016(validation set) with over 98% reduction, reconstruction MSE improves by 44.8%, and aperture classification training set accuracy reaches 78.98%(F1-score 75.52%). SNR improvement rates are 5.08% and 4.83%. Compared with traditional single-task models, the method achieves feature sharing and complementarity, reducing training costs and improving performance. Despite slight validation fluctuations in classification and reconstruction, the architecture shows strong potential in denoising and feature extraction. Future work will integrate Transformer and self-supervised learning to enhance generalization and engineering value.

KEYWORDS

Pipeline leak; Signal denoising; Multi-task learning; U-Net; Signal reconstruction; Classification detection

CITE THIS PAPER

Limi Chen, Yutong Yang, Research on Optimization of UAV Smoke Screen Jamming Bomb Deployment Strategy. Advances in Computer, Signals and Systems (2025) Vol. 9: 25-38. DOI: http://dx.doi.org/10.23977/acss.2025.090404.

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