An Inversion Method Based on Prior Knowledge for Deep Cascading Pipeline Defects

With the robust growth of the national economy, the demand for oil and natural gas continues to rise, heightening the significance of pipeline transportation in the energy sector. However, long-term pipeline operations are often subjected to factors such as corrosion, aging, and damage, which can result in leaks and safety incidents, posing significant threats to life, property, and environmental integrity. Consequently, timely and precise detection of pipeline defects and estimation of their sizes hold paramount practical importance. This research endeavors to employ advanced information technology and artificial intelligence to explore methods for pipeline defect detection and size estimation grounded in prior knowledge. The aim is to enhance the accuracy and reliability of pipeline defect diagnosis and ensure the safe operation of pipelines. The primary innovative work includes the development of a preprocessing method based on prior knowledge, the design of an adaptive algorithm for estimation of defect size, and the creation of an algorithm for estimation of deep cascade pipeline defect size. These methods effectively combine traditional mechanisms and data-driven approaches, leveraging the strengths of both to improve performance, accuracy, and robustness. The proposed methodology demonstrates superior accuracy and stability in defect inversion, providing strong technical support for the quantitative assessment of pipeline defects, which is significant for fault diagnosis and the precise maintenance of long-distance pipelines.