A Mechanism-Aided Bilevel Knowledge Transfer Framework for Pipeline Corrosion Condition Quantitative Assessment

Pipeline system, as a vital industrial infrastructure, inevitably suffers from corrosion due to long-term service, which poses great threat to safe and stable energy transport. Many intelligent quantitative assessment technologies have emerged to evaluate the pipeline corrosion condition. However, they heavily rely on the quality and quantity of corrosion data. For industrial scenarios, the acquisition of pipeline corrosion data is costly with precision equipment for excavation works, and the annotation task is also labor-intensive, which severely limits their applicability. To address these issues, a mechanism-aided bilevel knowledge transfer framework is proposed to achieve effective quantitative assessment of pipeline corrosion condition. First, a high-fidelity mechanism model is designed to simulate the pipeline corrosion process and generates sufficient simulation data to alleviate the dependence on real-field resources. Then, a bilevel distribution-aware idea is proposed to transfer knowledge from simulated to real corrosion data, which is our original effort to focus on both feature complexity and label finesse, so as to accommodate the real-world corrosion for sensible domain adaptation. Next, we propose the idea of divide-and-conquer based on variational integration embedding (VIE) to maximize model performance improvement with minimal expert workload, where unreliable corrosion data are asked to expert annotation, while others are utilized via pseudolabels obtained from VIE. In the experiments, the performance of our method based on both hardware test platform and practical application case is systematically validated, and the competitive results indicate that our method has great potential in intelligent pipeline systems.