论文标题
基于深度学习的智能硬币-TAP测试缺陷识别
Deep Learning based Intelligent Coin-tap Test for Defect Recognition
论文作者
论文摘要
硬币测试是一种方便且主要的方法，用于非破坏性测试，而手动的现场操作则很艰难且昂贵。借助最新的智能信号处理方法，卷积神经网络（CNN），我们实现了智能硬币测试，该测试在识别缺陷方面表现出卓越的性能。但是，CNN的成功依赖于相同情况下的许多标记的数据，这对于许多真正的工业实践可能很难获得。本文进一步开发了此问题的转移学习策略，即将对一个方案数据培训的模型转移到另一种方案。在实验中，结果通过使用领域适应和伪标签学习策略给出了显着的改进。因此，可以将模型应用于无需（小于10 \％）标记的数据的方案中，该数据采用了此处提出的转移学习策略。此外，我们在整个研究中都使用了一个基准数据集。此基准数据集用于https://github.com/pphub-hy/torch-tapnet上，包含大约100,000个声音信号的硬币-TAP测试。
The coin-tap test is a convenient and primary method for non-destructive testing, while its manual on-site operation is tough and costly. With the help of the latest intelligent signal processing method, convolutional neural networks (CNN), we achieve an intelligent coin-tap test which exhibited superior performance in recognizing the defects. However, this success of CNNs relies on plenty of well-labeled data from the identical scenario, which could be difficult to get for many real industrial practices. This paper further develops transfer learning strategies for this issue, that is, to transfer the model trained on data of one scenario to another. In experiments, the result presents a notable improvement by using domain adaptation and pseudo label learning strategies. Hence, it becomes possible to apply the model into scenarios with none or little (less than 10\%) labeled data adopting the transfer learning strategies proposed herein. In addition, we used a benchmark dataset constructed ourselves throughout this study. This benchmark dataset for the coin-tap test containing around 100,000 sound signals is published at https://github.com/PPhub-hy/torch-tapnet.
