An X-Ray-Based Multiexpert Inspection Method for Automatic Welding Defect Assessment in Intelligent Pipeline Systems
Fengyuan Zuo, Jinhai Liu, Mingrui Fu, Lei Wang, Zhen Zhao
Abstract
Welding is a key technical means to ensure the safe and stable connection between pipeline systems. The intelligent welding defect assessment based on X-ray inspection helps to improve the efficiency and accuracy of pipeline welding fault diagnosis. However, this work is highly challenging and systematic, and includes multiple critical subtasks, such as weld area extraction and defect detection. The existing methods have fallen into the quagmire of various task flow concatenation, resulting in unsatisfactory accuracy and efficiency. To overcome this dilemma, a multiexpert inspection method based on X-ray is proposed to perform welding defect assessment. First, a preprocessing method based on window width and window level adjustment is designed to calibrate original data to ensure high-quality input of the subsequent neural network. Second, a basic network layer with universal representation ability initialization is designed to extract discriminative multitask feature representations. Next, a novel task-specific experts is presented to perform weld area extraction and defect detection in a divide and conquer manner. Finally, a rule-based defect postprocessing is designed to evaluate the level of weld defects. In the experiment, the performance of defect detection expert and weld seam segmentation expert based on hardware testing platform is systematically verified and discussed. In practical defect assessment application cases, we obtained competitive results (recall: 98.5%, precision: 95.2%, accuracy: 97.4%). Hence, the proposed intelligent multiexpert method has shown great potential in actual pipeline weld maintenance and significantly saves operators time and costs.