Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature SelectionReport as inadecuate

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Department of Computer Science, Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA


Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3G1, Canada


Author to whom correspondence should be addressed.

Academic Editors: Ferran Martín and Jordi Naqui

Abstract 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. View Full-Text

Keywords: microwave sensors; nondestructive testing; feature selection; machine learning microwave sensors; nondestructive testing; feature selection; machine learning

Author: Abdelniser Moomen 1, Abdulbaset Ali 2 and Omar M. Ramahi 2,*



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