TY - GEN
T1 - A Flight Parameter-Based Flight Load Prediction Method for Aircraft Fatigue Life Monitoring via Maneuver Recognition and Deep Learning
AU - Cao, Shancheng
AU - Qi, Haoyu
AU - Xu, Chao
AU - Yin, Zhiping
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - The measured load spectrum is essential in evaluating the fatigue life and reliability of an aircraft. To boost the accuracy of flight load estimation, a novel method based on maneuver recognition and deep learning is proposed in this paper. In the aspect of maneuver recognition, a fast and accurate dynamic time warping algorithm is harnessed to effectively identify various maneuvers such as loop and half roll. Furthermore, for a certain determined flight maneuver, a transformer network with self-attention is adopted to establish the prediction model between flight parameters and flight loads. Finally, experimental results manifest that the proposed method can accurately estimate the operational flight loads based on flight parameters. In addition, a comparison with many other neural networks is conducted to demonstrate the advantages of the proposed method. This study provides significant supports in improving the fatigue life monitoring of an aircraft.
AB - The measured load spectrum is essential in evaluating the fatigue life and reliability of an aircraft. To boost the accuracy of flight load estimation, a novel method based on maneuver recognition and deep learning is proposed in this paper. In the aspect of maneuver recognition, a fast and accurate dynamic time warping algorithm is harnessed to effectively identify various maneuvers such as loop and half roll. Furthermore, for a certain determined flight maneuver, a transformer network with self-attention is adopted to establish the prediction model between flight parameters and flight loads. Finally, experimental results manifest that the proposed method can accurately estimate the operational flight loads based on flight parameters. In addition, a comparison with many other neural networks is conducted to demonstrate the advantages of the proposed method. This study provides significant supports in improving the fatigue life monitoring of an aircraft.
KW - Deep learning
KW - Flight load prediction
KW - Maneuver recognition
KW - Structural health monitoring
UR - http://www.scopus.com/inward/record.url?scp=85195587956&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-49421-5_88
DO - 10.1007/978-3-031-49421-5_88
M3 - 会议稿件
AN - SCOPUS:85195587956
SN - 9783031494208
T3 - Mechanisms and Machine Science
SP - 1073
EP - 1082
BT - Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023) - Volume 2
A2 - Ball, Andrew D.
A2 - Wang, Zuolu
A2 - Ouyang, Huajiang
A2 - Sinha, Jyoti K.
PB - Springer Science and Business Media B.V.
T2 - UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023
Y2 - 29 August 2023 through 1 September 2023
ER -