Research on cross-domain generative diagnosis for oil and gas pipeline defect based on limited field data

Junming Yao, Wei Liang and Zhongmin Xiao

Energy, 2025, vol. 319, issue C

Abstract: The intelligent defect diagnosis of oil and gas pipelines ensures the reliability and safety of energy transportation. Existing deep learning methods have been rapidly advanced in the field of fault diagnosis, but their excellent performances rely on a large amount of training samples. This paper conducts generative diagnosis research based on limited oil and gas pipeline defects, and proposes a novel Cross-Domain Generative Diagnosis Method (CDGM). First, defect signals are transformed into time-frequency images with stronger feature representation as model input. Then, a cross-domain collaborative training is constructed to simultaneously learn the feature distribution between the field and the simulated samples. During the generation process, the proposed cross-domain generation mechanism is used to constrain the noise disturbance level in real-time, continuously narrowing the feature distribution differences between generated and real samples in the field domain. In the generative diagnosis of pipeline defects, we have further studied the impact of different generation ratios and model structures on diagnostic accuracy. Experimental results demonstrate that CDGM has outstanding sample generation quality and cross-domain feature distribution performance, which can effectively improve the diagnostic accuracy, with an increase of 7.21%–12.79 %. This research has a positive impact on enhancing pipeline reliability and safety.

Keywords: Oil and gas pipeline; Sample generation; Defect diagnosis; Limited dataset; Deep learning (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:319:y:2025:i:c:s0360544225007285

DOI: 10.1016/j.energy.2025.135086

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