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A multilevel isolation forrest and convolutional neural network algorithm for impact characterization on composite structures
File(s)
sensors-20-05896.pdf (5.97 MB)
Published version
OA Location
Author(s)
Salehzadeh Nobari, Amin Ebrahim
Aliabadi, MH Ferri
Type
Journal Article
Abstract
In this paper, a Deep Learning approach is proposed to classify impact data based on the type of impact (Hard or Soft Impacts), via obtaining voltage signals from Piezo-Electric sensors, mounted on a composite panel. The data is processed further to be classified based on their energy, location and material. Minimalistic and Automated feature extraction and selection is achieved via a deep learning algorithm. Convolutional Neural Networks (CNN) are employed to extract and select important features from the voltage data. Once features are selected the impacts, are classified based on either, Hard Impacts (simulated from steel impactors in a lab setting), Soft Impacts (simulated from silicon impactors in a lab setting) and their corresponding location and energy levels. Furthermore, in order to use the right data for training they are obtained from the signals as anomalies via Isolation Forests (IF) to speed up the process. Using this approach Hard and Soft Impacts, their corresponding locations and respective energies are identified with high accuracy.
Date Acceptance
2020-10-19
Citation
Sensors, 20 (20), pp.5896-5896
URI
URL
DOI
ISSN
1424-8220
Publisher
MDPI AG
Start Page
5896
End Page
5896
Journal / Book Title
Sensors
Volume
20
Issue
20
Copyright Statement
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/)
License URL
Identifier
https://www.mdpi.com/1424-8220/20/20/5896
Subjects
0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
Analytical Chemistry
0502 Environmental Science and Management
0602 Ecology
Publication Status
Published
Date Publish Online
2020-10-19