Next Article in Journal
A Systematic Review of Detecting Sleep Apnea Using Deep Learning
Next Article in Special Issue
Fuzzy-Based Approach Using IoT Devices for Smart Home to Assist Blind People for Navigation
Previous Article in Journal
Synthesis, Photophysics, and Solvatochromic Studies of an Aggregated-Induced-Emission Luminogen Useful in Bioimaging
Article

A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures

1
Department of Aeronautics, Imperial College London, London SW7 2AZ, UK
2
Wolfson School of Mechanical, Electrical and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Sensors 2019, 19(22), 4933; https://doi.org/10.3390/s19224933
Received: 16 October 2019 / Revised: 3 November 2019 / Accepted: 10 November 2019 / Published: 12 November 2019
(This article belongs to the Special Issue Internet of Things for Structural Health Monitoring)
This paper reports on a novel metamodel for impact detection, localization and characterization of complex composite structures based on Convolutional Neural Networks (CNN) and passive sensing. Methods to generate appropriate input datasets and network architectures for impact localization and characterization were proposed, investigated and optimized. The ultrasonic waves generated by external impact events and recorded by piezoelectric sensors are transferred to 2D images which are used for impact detection and characterization. The accuracy of the detection was tested on a composite fuselage panel which was shown to be over 94%. In addition, the scalability of this metamodelling technique has been investigated by training the CNN metamodels with the data from part of the stiffened panel and testing the performance on other sections with similar geometry. Impacts were detected with an accuracy of over 95%. Impact energy levels were also successfully categorized while trained at coupon level and applied to sub-components with greater complexity. These results validated the applicability of the proposed CNN-based metamodel to real-life application such as composite aircraft parts. View Full-Text
Keywords: structural health monitoring (SHM); convolutional neural network (CNN); deep-learning; passive sensing; impact detection; impact characterization; composite structures structural health monitoring (SHM); convolutional neural network (CNN); deep-learning; passive sensing; impact detection; impact characterization; composite structures
Show Figures

Figure 1

MDPI and ACS Style

Tabian, I.; Fu, H.; Sharif Khodaei, Z. A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures. Sensors 2019, 19, 4933. https://doi.org/10.3390/s19224933

AMA Style

Tabian I, Fu H, Sharif Khodaei Z. A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures. Sensors. 2019; 19(22):4933. https://doi.org/10.3390/s19224933

Chicago/Turabian Style

Tabian, Iuliana, Hailing Fu, and Zahra Sharif Khodaei. 2019. "A Convolutional Neural Network for Impact Detection and Characterization of Complex Composite Structures" Sensors 19, no. 22: 4933. https://doi.org/10.3390/s19224933

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