Real-time Automatic Thickness Recognition Using Pulse Eddy Current with Deep Learning

被引：4

作者：

Meng, Tian ^{[1
]}

Xiong, Lei ^{[1
]}

Zheng, Xinnan ^{[1
]}

Xia, Zihan ^{[1
]}

Liu, Xiaofei ^{[1
]}

Tao, Yang ^{[1
]}

Yang, Wuqiang ^{[1
]}

Yin, Wuliang ^{[1
]}

机构：

[1] Univ Manchester, Dept Elect & Elect Engn, Manchester, Lancs, England

来源：

2023 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC | 2023年

关键词：

Pulse Eddy Current; Deep Learning; Embedded System; Real-time Recognition;

D O I：

10.1109/I2MTC53148.2023.10175900

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Pulse eddy current (PEC) is one of the key eddy current testing (ECT) techniques and it is widely used in metal industry. Being able to automatically recognize thickness with PEC can make the manufacturing process more efficient and convenient. In this paper, the main contribution is threefold. Firstly, a novel portable pulse eddy current device is designed, and a new PEC dataset is constructed with a wide variety of features using our device. Secondly, 1-D convolutional-based deep learning models are utilised to achieve automatic thickness recognition with high accuracy. In addition, models are moderately immune to lift-off and edge effects. Lastly, a compact and lightweight 1-D convolutional neural network is deployed on the STM32 microcontroller in our device, and it achieves realtime, accurate and low-latency automatic thickness recognition.

引用

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