A 1D-AE-PINN Crack Quantification Network Inspired by a Novel Physical Feature of ACFM

作者：Jianxi Ding, Xin’an Yuan, Wei Li, Baoping Cai, Xiaokang Yin, Xiao Li, Jianchao Zhao, Qinyu Chen, Xiangyang Wang, Zichen Nie, Qiyue Yin, Jianming Zhao · 发表于：IEEE Transactions on Industrial Informatics · 年份：2025 · DOI：10.1109/tii.2025.3586053 · 被引用次数：7 · 研究领域：Non-Destructive Testing Techniques、Ultrasonics and Acoustic Wave Propagation、Structural Health Monitoring Techniques

Alternating current field measurement (ACFM) is widely used in the quantitative detection of crack due to its advantages of noncontact measurement and high accuracy. However, the noncontact measurement introduces signal interference including constant and random lift-off. Both lift-offs bring challenges to the accurate quantification of cracks. It is difficult to obtain bothBxandBzsignals effectively. In this article, a new 1D-AE-PINN framework to accurately quantify the crack under the lift-off interference is proposed. A novel insight feature ofBxsignal with physical information about the crack size is studied and integrated into loss functions of the 1D-AE-PINN. The features encoded by 1D-AE-PINN are used as input to the quantization network. The advantages of 1D-AE-PINN in accuracy are proved by comparative experiments. The results show that the length and depth of the crack can be measured by onlyBxsignal. The mean squared errors of length and depth are 0.66 and 0.39 mm2.