Real-Time Tunnel-Magnetoresistive-Based Pulsed Eddy Current Testing With Deep Learning
Tian Meng, Lei Xiong, Xinnan Zheng, Yang Tao, Wuliang Yin
Abstract
Pulsed eddy current (PEC) testing is a non-destructive testing (NDT) technique that is widely used in the industry. The ability to detect corrosion in metal materials is crucial for ensuring safety and mitigating potential hazards. This paper provides a solution from hardware to software to show the ability of deep learning (DL) method to process PEC data under multiple complex distortions, and we also deliver a real-time DL edge computing system for metal thickness recognition. There are three key contributions. Firstly, we constructed a new dataset generated from our customdesigned PEC device with a diverse range of features. To better simulate real-world scenarios, measurement covers various thickness, lift-off, position, insulation and weather jacket conditions. Secondly, we adapted 1D convolutional neural network (CNN) to process the time series PEC data. This approach achieves high accurate thickness recognition, and the prediction is not affected by distortions, such as lift-off and edge effects. Lastly, we integrated a compact and tiny CNN into the STM32 microcontroller within our device. This edge computing system achieves real-time, accurate and low-latency thickness recognition. Our study represents a significant advancement towards the development of automated thickness recognition technologies in the NDT based on embedded DL. Our dataset and DL models are publicly available at Kaggle <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>a</i></sup> and GitHub <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>b</i></sup> .