Non-Destructive Testing Techniques
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Deep learning with ECT technology is integrated, focusing on the depth classification task of defects in stainless steel plates, and shows that the constructed model can effectively extract deep features from 1D ECT signals, improving the accuracy and robustness of depth classification for defects in stainless steel plates.
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Abstract As a non-destructive testing technique, Eddy Current Testing (ECT) is widely applied in the industrial field, especially suitable for defect detection of non-magnetic metal components such as stainless steel. However, traditional ECT technology still has obvious limitations. This paper integrates deep learning with ECT technology, focusing on the depth classification task of defects in stainless steel plates. Taking ResNet as the backbone network, a multi-scale convolution module and channel attention mechanisms are embedded to construct an intelligent detection model adapted to 1D ECT signals. In the research, a standardization method is adopted to preprocess ECT signals, retaining key features related to defect depth. Combined with the cosine annealing learning rate scheduling strategy with warm restarts, the convergence performance of the model is guaranteed, and the effectiveness of the model is verified through ablation experiments. The results show that the constructed model can effectively extract deep features from 1D ECT signals, improving the accuracy and robustness of depth classification for defects in stainless steel plates.
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@article{Zhao2026Defect,
title = {Defect Depth Classification for Deep Surface-Opening Defects in Stainless Steel Plates Based on Eddy Current Testing},
author = {Qian Zhao and Huilin Zhang and Kaihao Shen and Yujie Liu and Wuliang Yin},
journal = {Engineering Research Express},
year = {2026},
doi = {10.1088/2631-8695/aeb180},
url = {https://doi.org/10.1088/2631-8695/aeb180}
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