| 钦 杰,张力平,叶云飞,胡 鹏,蔺宏良.一种基于卷积神经网络的电涡流金属辨识方法[J].电子测量与仪器学报,2020,34(4):172-179 |
| 一种基于卷积神经网络的电涡流金属辨识方法 |
| Metal type identification method based on convolutional neural network and eddy current |
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| DOI： |
| 中文关键词: 涡流 卷积神经网络 金属辨识 铁素体 珠光体 |
| 英文关键词:eddy currents CNN metal identification ferrite pearlite |
| 基金项目:长安大学陕西省高速公路施工机械重点实验室开放基金(300102259513)、中央高校基本科研业务费专项(300102258205)、江苏省
高校自然科学研究面上项目(17KJB510033)、江苏高校“青蓝工程”资助项目 |
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| 中文摘要: |
| 为实现对主要金相组织同为铁素体和珠光体的 3 种碳素结构钢的辨识,提出一种基于卷积神经网络的金属辨识方法。
卷积神经网络可以很好地处理环境信息复杂、推理规则不明确和样品本身有缺陷情况下的分类,利用涡流无损检测技术和卷积
神经网络算法搭建了该金属辨识平台,首先在涡流传感器的工作频率范围内随机选取 8 个高频点,并通过该传感器分别采集各
个频点下金属的信息;然后通过傅里叶变换、坐标变换等数据处理使得每种金属的信息图像化;最终通过卷积神经网络训练来
获得辨识模型。 结果表明,该方案对比传统方式可在不损伤金属的情况下识别金属;对比现有的 BP 神经网络算法(86. 20%),
对 3 种金属的正确识别率都达到了 92. 33%。 |
| 英文摘要: |
| In order to identify three types of carbon structural steels whose metallographic structures are ferrite and pearlite. This paper
proposes a metal identification method based on convolutional neural network. Convolutional neural networks can efficiently implement
classification with complex environmental information, ambiguous inference rules, and flawed samples. The metal identification platform
was built based on eddy current non-destructive testing technology and convolutional neural network. First, 8 high-frequency points are
randomly selected from the bandwidth of the eddy current sensor, and the metal information that under each frequency point is separately
collected by this eddy current sensor. Then, this information is imaged through data processing such as Fourier transform and coordinate
transformation. Finally, the identification model is obtained by convolutional neural network. The results show that the proposed scheme
can identify metals without damaging the metal compared to the traditional method. The accuracy of the CNN model for all three metals
increased to 92. 33%, which is superior to the BP neural network (86. 20%). |
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