Corrosion leakage risk diagnosis of oil and gas pipelines based on semi-supervised domain generalization model

Pipeline corrosion will lead to leakage, significantly affecting pipeline reliability and transportation safety. Accurate leakage diagnosis is vital to the operational safety of the oil and gas industry. However, current supervised learning diagnosis methods are limited in addressing cross-domain problems and limited labeled fault samples. And the potential leakage which has the leakage risk is difficult to diagnosis. Therefore, we propose a novel semi-supervised domain generalization method for leakage diagnosis based on laser optical sensing technology. An improved auxiliary classifier generative adversarial network (IACGAN) is developed with new structure and loss function to extract discriminative features. The Capsule network is improved with DenseBlock (D-CapsNet) for determining the leakage situation of source domain and unseen target domain. To make full use of limited data, the metric learning is combined with pseudo-label strategy in semi-supervised learning to enhance feature representations. The experimental results demonstrate that the domain generalization model performs well in cross-domain leakage diagnosis, where the potential leakage risk can also be accurately recognized. The average recognition accuracy is greater than 95%, which has better diagnosis accuracy than other state-of-the-art methods.