• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection
【2h】

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

机译：利用机器学习特征选择降低微波无损检测中的扫描频率

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms.
机译：对材料的健康状况进行无损检测（NDT）评估有助于从不健康的结构中对健康进行分类，或检测金属或介电结构中的缺陷。使用相同的测试设备对涂覆/未涂覆的金属或介电材料进行结构健康测试需要一种可以在金属和介电材料上使用的测试方法，例如微波测试。减少与当前结构健康的微波NDT诊断实践相关的复杂性和费用，需要一种基于特征选择和机器学习分类技术的有效且智能的方法。当前的微波NDT方法通常基于测量传感器整个工作频率范围内S矩阵的变化。例如，使用微波传感器评估金属结构的健康状况取决于反射或/和透射系数的测量结果，该测量结果是工作频带的扫描频率的函数。这项工作的目的是使用机器学习特征选择技术来降低扫描频率。通过将扫描频率视为特征，可以识别出最重要的特征的数量，然后在构建微波NDT设备时仅考虑最具影响力的特征（频率）。提出的降低扫描频率的方法已通过使用波导传感器和具有不同裂缝的金属板进行了实验验证。在研究的特征选择技术中，有信息增益，增益比，浮雕，卡方。通过对各种分类模型的性能评估来验证所选功能的有效性；即最近邻居，神经网络，随机森林和支持向量机。使用特征选择算法后，结果显示出良好的裂纹分类准确率。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

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