Next Article in Journal
An Adaptive Network Coding Scheme for Multipath Transmission in Cellular-Based Vehicular Networks
Previous Article in Journal
Label-Free Optical Resonator-Based Biosensors
Article

A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite Structures

Department of Aeronautics, Imperial College London, Exhibition Road, South Kensington, London SW7 2AZ, UK
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(20), 5896; https://doi.org/10.3390/s20205896
Received: 18 August 2020 / Revised: 26 September 2020 / Accepted: 16 October 2020 / Published: 19 October 2020
(This article belongs to the Section Electronic Sensors)
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. View Full-Text
Keywords: Piezo-Electric sensors; Convolutional Neural Networks; minimalistic and automated; feature extraction; Isolation Forests Piezo-Electric sensors; Convolutional Neural Networks; minimalistic and automated; feature extraction; Isolation Forests
Show Figures

Figure 1

MDPI and ACS Style

Salehzadeh Nobari, A.E.; Aliabadi, M.H.F. A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite Structures. Sensors 2020, 20, 5896. https://doi.org/10.3390/s20205896

AMA Style

Salehzadeh Nobari AE, Aliabadi MHF. A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite Structures. Sensors. 2020; 20(20):5896. https://doi.org/10.3390/s20205896

Chicago/Turabian Style

Salehzadeh Nobari, Amin E., and M.H.Ferri Aliabadi. 2020. "A Multilevel Isolation Forrest and Convolutional Neural Network Algorithm for Impact Characterization on Composite Structures" Sensors 20, no. 20: 5896. https://doi.org/10.3390/s20205896

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

Article Access Map by Country/Region

1
Back to TopTop