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Article

Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks

1
Center of Information Services and High Performance Computing (ZIH), Technische Universität Dresden, 01187 Dresden, Germany
2
Institute of Lightweight Engineering and Polymer Technology (ILK), Technische Universität Dresden, 01307 Dresden, Germany
3
Dresden Center for Intelligent Materials (DCIM), Technische Universität Dresden, 01069 Dresden, Germany
*
Author to whom correspondence should be addressed.
Academic Editor: Yolanda Vidal
Sensors 2021, 21(6), 2005; https://doi.org/10.3390/s21062005
Received: 24 February 2021 / Revised: 9 March 2021 / Accepted: 10 March 2021 / Published: 12 March 2021
(This article belongs to the Special Issue Vibration Sensor-Based Diagnosis Technologies and Systems: Part Ⅰ )
Damage identification of composite structures is a major ongoing challenge for a secure operational life-cycle due to the complex, gradual damage behaviour of composite materials. Especially for composite rotors in aero-engines and wind-turbines, a cost-intensive maintenance service has to be performed in order to avoid critical failure. A major advantage of composite structures is that they are able to safely operate after damage initiation and under ongoing damage propagation. Therefore, a robust, efficient diagnostic damage identification method would allow monitoring the damage process with intervention occurring only when necessary. This study investigates the structural vibration response of composite rotors by applying machine learning methods and the ability to identify, localise and quantify the present damage. To this end, multiple fully connected neural networks and convolutional neural networks were trained on vibration response spectra from damaged composite rotors with barely visible damage, mostly matrix cracks and local delaminations using dimensionality reduction and data augmentation. A databank containing 720 simulated test cases with different damage states is used as a basis for the generation of multiple data sets. The trained models are tested using k-fold cross validation and they are evaluated based on the sensitivity, specificity and accuracy. Convolutional neural networks perform slightly better providing a performance accuracy of up to 99.3% for the damage localisation and quantification. View Full-Text
Keywords: dense neural networks; convolutional neural networks; composites; fully connected neural networks; composite rotors; structural health monitoring (SHM); machine learning dense neural networks; convolutional neural networks; composites; fully connected neural networks; composite rotors; structural health monitoring (SHM); machine learning
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MDPI and ACS Style

Scholz, V.; Winkler, P.; Hornig, A.; Gude, M.; Filippatos, A. Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks. Sensors 2021, 21, 2005. https://doi.org/10.3390/s21062005

AMA Style

Scholz V, Winkler P, Hornig A, Gude M, Filippatos A. Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks. Sensors. 2021; 21(6):2005. https://doi.org/10.3390/s21062005

Chicago/Turabian Style

Scholz, Veronika, Peter Winkler, Andreas Hornig, Maik Gude, and Angelos Filippatos. 2021. "Structural Damage Identification of Composite Rotors Based on Fully Connected Neural Networks and Convolutional Neural Networks" Sensors 21, no. 6: 2005. https://doi.org/10.3390/s21062005

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