Toward semi-unattended operation condition monitoring for liquid energy pipelines: deep learning-enhanced autonomous reliability diagnosis

The growth of liquid energy pipeline networks has promoted economic development but also pose significant challenges for monitoring pipeline operating conditions. Conventional manual analysis, grounded in pipeline operating principles, is highly reliable but extremely labor-intensive and time-consuming. As a priori inference process, AI-driven intelligent monitoring methods alleviate the burden of manual work, yet they lack a means to verify the reliability and accuracy of outputs because of the interpretability lack. This study proposes an intelligent condition recognition and reliability diagnosis framework to achieve semi-unattended pipeline condition monitoring. The contributions of neurons in recognition model are visualized to obtain the hydraulic spatiotemporal explanations. Subsequently, a hybrid model, that incorporates the pretraining and a condition-prompted contrastive fine-tuning, is developed to achieve autonomous discrimination of explanations and reliability diagnosis of recognitions. The framework diagnoses the misrecognized and non-causal recognitions autonomously based on fully observable model inference logic. By relying on the expert knowledge and pipeline principles, inference logics that are inconsistent with pipeline operational knowledge are rectified. This framework prevents misrecognition of operating conditions from hindering timely maintenance, which could otherwise lead to safety-critical failures. Moving forward, the framework further establishes a technical paradigm for deploying AI within pipeline systems to achieve semi-unattended intelligent condition monitoring.