• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International Conference on Control, Automation and Systems >Identification of normal and abnormal from ultrasound images of power devices using VGG16
【24h】

Identification of normal and abnormal from ultrasound images of power devices using VGG16

机译：使用vgg16从电动设备超声图像识别正常和异常

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Power devices are semiconductor devices that handle high voltages and large currents, which are used in electric vehicles, televisions, and trains. Therefore, high reliability and safety are required, and to ensure this, power cycle tests are performed to analyze the breakdown process. Conventional tests are often difficult to analyze due to the influence of sparks generated during the test. Therefore, new tests are being developed by adding ultrasound to conventional methods. The new technology is capable of continuously recording structural changes inside the device during testing, which is expected to make testing much easier than conventional testing. However, the new technology still has some challenges. The main problems are the lack of a method for analyzing large amounts of image data and the extraction of small changes in image features that are difficult to distinguish with the human eye, and the establishment of such a system is required. In this paper, we use deep learning for image classification of the obtained ultrasound images. We propose a new network model with the addition of Batch normalization and Global average pooling to VGG16, which is a pre-trained model. In the experiment, accuracy=98.29%, TPR=98.96% and FPR=7.43% classification accuracy was obtained.
机译：功率器件是处理高电压和大电流的半导体器件，其用于电动车辆，电视和列车。因此，需要高可靠性和安全性，并确保这一点，执行功率循环测试以分析击穿过程。由于在测试期间产生的火花的影响，常规测试通常难以分析。因此，通过向常规方法添加超声波来开发新测试。新技术能够在测试期间连续记录设备内的结构变化，这预计将使测试比传统测试更容易。但是，新技术仍然存在一些挑战。主要问题是缺乏用于分析大量图像数据的方法以及难以区分人眼的图像特征的小变化的提取，并且需要建立这种系统。在本文中，我们使用深度学习获得所获得的超声图像的图像分类。我们提出了一种新的网络模型，添加了批量归一化和全局平均池到VGG16，这是一个预先训练的模型。在实验中，精度= 98.29％，TPR = 98.96％，获得了FPR = 7.43％的分类精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号