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In recent years, deep learning has been widely applied in the diagnosis of oil and gas pipeline failures due to its powerful feature representation capabilities. However, in practical applications, the diagnostic accuracy falls short of meeting actual requirements due to complex environmental interference, coupled with existing methods that focus solely on spatial feature relationships. To improve diagnostic performance, this paper eliminates the influence of complex backgrounds through optimal neural-immune domain in the image preprocessing stage. Subsequently, an Immune Depth Presentation Network capable of effectively capturing inter-feature correlations in the channel dimension is proposed, and it is integrated with a conventional convolutional neural network to construct the Immune Depth Presentation Convolutional Neural Network model. To validate the effectiveness and stability of the model, we compared the proposed algorithm in this paper with classic algorithms such as BPNN, KNN, VGG16, VGG19, Resnet34, and Resnet50. We conducted multiple experiments, and the results indicate that the proposed algorithm achieved the highest average testing accuracy, demonstrating stable performance.

The Immune Depth Presentation Convolutional Neural Network Used for Oil and Gas Pipeline Fault Diagnosis