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Sensors 2018, 18(1), 152; https://doi.org/10.3390/s18010152

Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors

1
Department of Electrical and Electronic, Mato Grosso Federal Institute of Technology, Cuiabá 78005-200, Brazil
2
Institute of Computing, Federal University of Mato Grosso, Cuiabá 78060-900, Brazil
3
Centre of Excellence in Engineered Fiber Composites, Faculty of Engineering and Surveying, University of Southern Queensland (USQ), Toowoomba, QLD 4667, Australia
*
Author to whom correspondence should be addressed.
Received: 15 November 2017 / Revised: 28 December 2017 / Accepted: 5 January 2018 / Published: 8 January 2018
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Abstract

A considerable amount of research has focused on monitoring structural damage using Structural Health Monitoring (SHM) technologies, which has had recent advances. However, it is important to note the challenges and unresolved problems that disqualify currently developed monitoring systems. One of the frontline SHM technologies, the Electromechanical Impedance (EMI) technique, has shown its potential to overcome remaining problems and challenges. Unfortunately, the recently developed neural network algorithms have not shown significant improvements in the accuracy of rate and the required processing time. In order to fill this gap in advanced neural networks used with EMI techniques, this paper proposes an enhanced and reliable strategy for improving the structural damage detection via: (1) Savitzky–Golay (SG) filter, using both first and second derivatives; (2) Probabilistic Neural Network (PNN); and, (3) Simplified Fuzzy ARTMAP Network (SFAN). Those three methods were employed to analyze the EMI data experimentally obtained from an aluminum plate containing three attached PZT (Lead Zirconate Titanate) patches. In this present study, the damage scenarios were simulated by attaching a small metallic nut at three different positions in the aluminum plate. We found that the proposed method achieves a hit rate of more than 83%, which is significantly higher than current state-of-the-art approaches. Furthermore, this approach results in an improvement of 93% when considering the best case scenario. View Full-Text
Keywords: SHM; electromechanical impedance; fuzzy ARTMAP network; probabilistic neural network; artificial intelligence; Euclidean distance; piezoelectricity; pattern recognition SHM; electromechanical impedance; fuzzy ARTMAP network; probabilistic neural network; artificial intelligence; Euclidean distance; piezoelectricity; pattern recognition
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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).
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de Oliveira, M.A.; Araujo, N.V.S.; da Silva, R.N.; da Silva, T.I.; Epaarachchi, J. Use of Savitzky–Golay Filter for Performances Improvement of SHM Systems Based on Neural Networks and Distributed PZT Sensors. Sensors 2018, 18, 152.

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