A task-oriented physical collaborative network for pipeline defect diagnosis in a magnetic flux leakage detection system

Defect diagnosis based on magnetic flux leakage (MFL) signals is an important process for assessing pipeline health, including defect detection and size quantification. However, existing studies suffer from poor consistency of results, because they regard defect detection and size quantification as separate tasks, lacking paradigm harmonization and interaction. In addition, the calibration of experts is required to achieve harmonization between the two, which increases the time cost of data analysis. To address the above challenges, our motivation is to synergistically learn two tasks within a unified framework and utilize their task properties for mutual benefit. Therefore, a novel defect diagnosis method based on a task-oriented physical collaborative network (TOPC-Net) is proposed, which is the first attempt at joint defect detection and size quantification in MFL inspection. First, a feature extraction subnetwork with a heterogeneous focus module is proposed to decompose initial task-specific features from shared spaces. Second, considering the strong correlation between the two tasks, a cross-task information awareness method is proposed to realize the information interaction between the two tasks, so that the task-specific features can be enhanced. Finally, a physical information-guided collaborative decision subnetwork is proposed to jointly optimize two tasks, where MFL domain knowledge is embedded into the subnetwork to provide expert guidance, ensuring the accuracy and stability of predictions. Experimental results show that the proposed method outperforms existing methods, with a detection accuracy of 96.0% and an average improvement of 7.5% in quantification accuracy, which makes it promising for industrial applications.