Next Article in Journal
A Component-Based Vocabulary-Extensible Sign Language Gesture Recognition Framework
Next Article in Special Issue
Using the Fingerprinting Method to Customize RTLS Based on the AoA Ranging Technique
Previous Article in Journal
Error Analysis of Clay-Rock Water Content Estimation with Broadband High-Frequency Electromagnetic Sensors—Air Gap Effect
Previous Article in Special Issue
Compact Reconfigurable Antenna with an Omnidirectional Pattern and Four Directional Patterns for Wireless Sensor Systems
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(4), 559; doi:10.3390/s16040559

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

1
Department of Computer Science, Rochester Institute of Technology, 1 Lomb Memorial Drive, Rochester, NY 14623, USA
2
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
Received: 16 February 2016 / Revised: 9 April 2016 / Accepted: 14 April 2016 / Published: 19 April 2016
View Full-Text   |   Download PDF [2501 KB, uploaded 19 April 2016]   |  

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
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Moomen, A.; Ali, A.; Ramahi, O.M. Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection. Sensors 2016, 16, 559.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top