Version 1
: Received: 5 June 2024 / Approved: 6 June 2024 / Online: 10 June 2024 (12:37:56 CEST)
How to cite:
Ballarin, P.; Sala, G.; Macchi, M.; Roda, I.; Baldi, A.; Airoldi, A. Damage Identification in a Helicopter Rotor Blade Root in Realistic Load Conditions With Neural Network-Based Algorithms. Preprints2024, 2024060421. https://doi.org/10.20944/preprints202406.0421.v1
Ballarin, P.; Sala, G.; Macchi, M.; Roda, I.; Baldi, A.; Airoldi, A. Damage Identification in a Helicopter Rotor Blade Root in Realistic Load Conditions With Neural Network-Based Algorithms. Preprints 2024, 2024060421. https://doi.org/10.20944/preprints202406.0421.v1
Ballarin, P.; Sala, G.; Macchi, M.; Roda, I.; Baldi, A.; Airoldi, A. Damage Identification in a Helicopter Rotor Blade Root in Realistic Load Conditions With Neural Network-Based Algorithms. Preprints2024, 2024060421. https://doi.org/10.20944/preprints202406.0421.v1
APA Style
Ballarin, P., Sala, G., Macchi, M., Roda, I., Baldi, A., & Airoldi, A. (2024). Damage Identification in a Helicopter Rotor Blade Root in Realistic Load Conditions With Neural Network-Based Algorithms. Preprints. https://doi.org/10.20944/preprints202406.0421.v1
Chicago/Turabian Style
Ballarin, P., Andrea Baldi and Alessandro Airoldi. 2024 "Damage Identification in a Helicopter Rotor Blade Root in Realistic Load Conditions With Neural Network-Based Algorithms" Preprints. https://doi.org/10.20944/preprints202406.0421.v1
Abstract
Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMS) perform real-time monitoring functions, but their performances must be carefully assessed. In this work the damage detection performances of a strain-based SHMS, were evaluated on a composite helicopter rotor blade root. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through Finite Element analyses performed on the component. The effects of the loads variability and of damages were decoupled by including a load recognition step in the algorithm. Load recognition was accomplished in different ways: version#1 adopted an Artificial Neural Network (ANN) trained on blades in pristine condition; version#2 was trained also on damaged blades; version#3 adopted a calibration matrix method. Anomaly detection, damage assessment, and localization were performed by using an ANN. Results showed a higher load identification and anomaly detection accuracy for version#1 and version#2 compared to version#3. Gaussian noise reduced anomaly detection and damage assessment performances, and reduced damage localization accuracy for bigger damages. The damage detection performance were obtained for a fibre optic based-SHMS, including the trade-off between probability of detection and false alarm rate for different damage sizes.
Copyright:
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.
