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Open AccessArticle

Classification of Micro-Damage in Piezoelectric Ceramics Using Machine Learning of Ultrasound Signals

1
Department of Electronics Engineering, Indian Institute of Technology, Dhanbad 826004, India
2
Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
3
Department of Computer Science, UiT The Arctic University of Norway, 9037 Tromsø, Norway
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(19), 4216; https://doi.org/10.3390/s19194216
Received: 15 August 2019 / Revised: 19 September 2019 / Accepted: 26 September 2019 / Published: 28 September 2019
(This article belongs to the Section Physical Sensors)
Ultrasound based structural health monitoring of piezoelectric material is challenging if a damage changes at a microscale over time. Classifying geometrically similar damages with a difference in diameter as small as 100 μ m is difficult using conventional sensing and signal analysis approaches. Here, we use an unconventional ultrasound sensing approach that collects information of the entire bulk of the material and investigate the applicability of machine learning approaches for classifying such similar defects. Our results show that appropriate feature design combined with simple k-nearest neighbor classifier can provide up to 98% classification accuracy even though conventional features for time-series data and a variety of classifiers cannot achieve close to 70% accuracy. The newly proposed hybrid feature, which combines frequency domain information in the form of power spectral density and time domain information in the form of sign of slope change, is a suitable feature for achieving the best classification accuracy on this challenging problem. View Full-Text
Keywords: structural health monitoring; ultrasound; feature design; classification structural health monitoring; ultrasound; feature design; classification
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Tripathi, G.; Anowarul, H.; Agarwal, K.; Prasad, D.K. Classification of Micro-Damage in Piezoelectric Ceramics Using Machine Learning of Ultrasound Signals. Sensors 2019, 19, 4216.

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