A Single-Stage Enhancement-Identification Framework for Pipeline MFL Inspection

• Published in: IEEE transactions on instrumentation and measurement Vol. 71; pp. 1 - 13
• Main Authors: Feng, Jian, Zhang, Xinbo, Lu, Senxiang, Yang, Feiran
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data processing; Defect identification; fault diagnosis; Feature extraction; Feature maps; Heating systems; Identification; Industrial applications; Inspection; Interference; Magnetic flux; magnetic flux leakage (MFL); Magnetic flux leakage testing; Magnetic separation; neural network; Nondestructive testing; pipeline inspection; Pipelines; Pipes; Signal processing; Signal resolution
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2022.3176285

Magnetic flux leakage (MFL) inspection, one of the nondestructive testing methods, has been widely applied in pipeline maintenance. In pipeline MFL data processing, defect identification is a crucial step, which aims at measuring the locations of defect MFL signals in MFL heat maps. MFL signals collected from the pipeline are not ideal, containing noise and interference. In this case, measuring the locations of defect MFL signals, especially the locations of weak defect MFL signals, is a challenge. To address this challenge, an enhancement process is required to coordinate with the identification process. In this article, two separated processes, enhancement and identification, are integrated into a single-stage framework, aiming at improving the defect identification performance by strengthening the differences between defect signal areas and pipe wall signal areas. The proposed framework can enhance the defect areas purposefully and ignore the noise and interference in nondefect areas, which promotes the measuring effect for locations of defect MFL signals. In the proposed method, an enhancement module is constructed to upsample the MFL heat maps, and the resolution of feature maps in the framework is increased to <inline-formula> <tex-math notation="LaTeX">288 \times 600 </tex-math></inline-formula>. A novel loss function is designed, and the gray value contrast between defect signal areas and pipe wall signal areas in MFL heat maps is enhanced from 10 to 48 approximately through task-oriented joint training. The proposed method achieves 0.967 average precision (AP), and the identification accuracy is improved to 97.3%. In addition, the average deviations of identified defect signals are reduced by around 2-9 mm, and the uncertainties are reduced to 0.27-0.35 mm. The experiment results validate the superiority of the proposed framework in industrial applications.