Author(s)

Yingtong Wan ¹, Haozhe Jin ², Songhang Wu ¹, Junhao Zhao ¹, Kan Zhu ¹

Affiliation(s)

¹ Qixin Honors School, Zhejiang Sci-Tech University, Hangzhou, 310018, China
² School of Mechanical Engineering, Zhejiang Sci-Tech University, Hangzhou, 310018, China

Corresponding Author

Yingtong Wan

ABSTRACT

Far-field eddy current detection technology is a special eddy current technology that utilizes electromagnetic effect to detect the pipe through the wall. This paper introduces the principle of the far-field eddy current detection technology, including the two propagation modes of direct coupling and indirect coupling, and explains the division of different regions and their effects. The design and composition of the far-field eddy current detection device are then presented. In addition, this paper discusses the application of machine learning in analyzing far-field eddy current detection data, including the basic principles of machine learning, data preprocessing, data segmentation and model building steps. Finally, the evaluation indexes of machine learning models are introduced, as well as specific algorithms and evaluation indexes that may be used in far-field eddy current detection.

KEYWORDS

Far-field eddy current detection technology, Machine learning, Industrial Piping

CITE THIS PAPER

Yingtong Wan, Haozhe Jin, Songhang Wu, Junhao Zhao, Kan Zhu, Machine learning-based real-time detection of residual wall thickness in industrial pipelines. Automation and Machine Learning (2024) Vol. 5: 1-8. DOI: http://dx.doi.org/10.23977/autml.2024.050201.

REFERENCES

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