A FENN-TL Approach for Reliability Analysis of a Primary Ice Detection System
Publication: Journal of Aerospace Engineering
Volume 36, Issue 6
Abstract
Solving the reliability problem of primary ice detection systems is of great significance to support the design of anti-icing systems. In this paper, an efficient method employing a feature-enhanced neural network (FENN)–transfer learning (TL) surrogate model was developed to process two types of features (flight and aircraft parameters). A FENN was established with an autoencoder, and TL was implemented with 15 new points. A new loss function was designed and combined with FENN to control the direction of prediction error. The determination coefficient was 0.993 in the holding state and 0.997 in the local area near the dangerous state. Based on 1 million predicted results of Common Research Model (CRM) airfoil, the primary ice detection system is most likely to have reliability problems at a low angle of attack and low-speed flight state, and angle of attack has the greatest influence. FENN-TL proved a flexible and efficient method for reliability analysis of primary ice detection systems. This method and the obtained CRM results can be further used to support the design and airworthiness certification of large aircraft.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
The data that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
We thank the Center for High Performance Computing of SJTU for providing the supercomputer to support this research. This work is financially supported by the National Key R&D Program of China, Project No. 2020YFA0712000.
References
Brunton, S. L., B. R. Noack, and P. Koumoutsakos. 2020. “Machine learning for fluid mechanics.” Annu. Rev. Fluid Mech. 52 (5): 477–508. https://doi.org/10.1146/annurev-fluid-010719-060214.
Cai, W., J. Huang, A. Deng, and Q. Wang. 2021. “Volumetric reconstruction for combustion diagnostics via transfer learning and semi-supervised learning with limited labels.” Aerosp. Sci. Technol. 110 (Aug): 106487. https://doi.org/10.1016/j.ast.2020.106487.
Chollet, F. 2021. Deep learning with Python. New York: Simon and Schuster.
Code of Federal Regulations. 2002. 25: Airworthiness standards: Transport category airplanes. Washington, DC: Federal Aviation Administration.
Eiskowitz, S. S., E. Crawley, and B. Cameron. 2021. Forecasting internet demand in a satellite communication network using transfer learning. Reston, VA: American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2021-4007.
Forrester, A. I., and A. J. Keane. 2009. “Recent advances in surrogate-based optimization.” Prog. Aerosp. Sci. 45 (1–3): 50–79. https://doi.org/10.1016/j.paerosci.2008.11.001.
Funahashi, K.-I., and Y. Nakamura. 1993. “Approximation of dynamical systems by continuous time recurrent neural networks.” Neural Networks 6 (6): 801–806. https://doi.org/10.1016/S0893-6080(05)80125-X.
Giorgiani do Nascimento, R., and F. Viana. 2020. “Satellite image classification and segmentation with transfer learning.” In Proc., AIAA Scitech 2020 Forum. Reston, VA: American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2020-1864.
Guo, X., W. Li, and F. Iorio. 2016. “Convolutional neural networks for steady flow approximation.” In Proc., 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, 481–490. New York: Association for Computing Machinery. https://doi.org/10.1145/2939672.2939738.
Hornik, K., M. Stinchcombe, and H. White. 1989. “Multilayer feedforward networks are universal approximators.” Neural Networks 2 (5): 359–366. https://doi.org/10.1016/0893-6080(89)90020-8.
Hui, X., J. Bai, H. Wang, and Y. Zhang. 2020. “Fast pressure distribution prediction of airfoils using deep learning.” Aerosp. Sci. Technol. 105 (1): 105949. https://doi.org/10.1016/j.ast.2020.105949.
Jun, T., S. Gang, G. Liqiang, and W. Xinyu. 2020. “Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization.” Chin. J. Aeronaut. 33 (6): 1573–1588. https://doi.org/10.1016/j.cja.2020.01.015.
Kingma, D. P., and J. Ba. 2014. “Adam: A method for stochastic optimization.” Preprint, submitted December 22, 2014. http://arxiv.org/abs/arXiv:1412.6980.
Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. “Imagenet classification with deep convolutional neural networks.” Adv. Neural Inf. Process. Syst. 60 (6): 84–90.
Lee, S., and D. You. 2019. “Data-driven prediction of unsteady flow over a circular cylinder using deep learning.” J. Fluid Mech. 879 (Jun): 217–254. https://doi.org/10.1017/jfm.2019.700.
Lei, R., J. Bai, H. Wang, B. Zhou, and M. Zhang. 2021. “Deep learning based multistage method for inverse design of supercritical airfoil.” Aerosp. Sci. Technol. 119 (Aug): 107101. https://doi.org/10.1016/j.ast.2021.107101.
Li, K., J. Kou, and W. Zhang. 2019. “Deep neural network for unsteady aerodynamic and aeroelastic modeling across multiple Mach numbers.” Nonlinear Dyn. 96 (3): 2157–2177. https://doi.org/10.1007/s11071-019-04915-9.
Li, K., J. Kou, and W. Zhang. 2021. “Unsteady aerodynamic reduced-order modeling based on machine learning across multiple airfoils.” Aerosp. Sci. Technol. 119 (Dec): 107173. https://doi.org/10.1016/j.ast.2021.107173.
Mallick, M., A. Mohanta, A. Kumar, and K. Charan Patra. 2020. “Prediction of wind-induced mean pressure coefficients using GMDH neural network.” J. Aerosp. Eng. 33 (1): 04019104. https://doi.org/10.1061/(ASCE)AS.1943-5525.0001101.
Mason, J. G., J. W. Strapp, and P. Chow. 2006. “The ice particle threat to engines in flight.” In Proc., 44th AIAA Aerospace Sciences Meeting and Exhibit. San Jose, CA: Curran Associates.
Paszke, A., et al. 2017. “Automatic differentiation in PyTorch.” In NIPS Autodiff Workshop. Red Hook, NY: Curran Associates.
Paszke, A., et al. 2019. “PyTorch: An imperative style, high-performance deep learning library.” In Vol. 32 of Advances in neural information processing systems. San Jose, CA: Curran Associates.
Queipo, N. V., R. T. Haftka, W. Shyy, T. Goel, R. Vaidyanathan, and P. K. Tucker. 2005. “Surrogate-based analysis and optimization.” Prog. Aerosp. Sci. 41 (1): 1–28. https://doi.org/10.1016/j.paerosci.2005.02.001.
Saltelli, A. 2002. “Sensitivity analysis for importance assessment.” Risk Anal. 22 (3): 579–590. https://doi.org/10.1111/0272-4332.00040.
Saltelli, A., M. Ratto, T. Andres, F. Campolongo, J. Cariboni, D. Gatelli, M. Saisana, and S. Tarantola. 2008. Global sensitivity analysis: The primer. New York: Wiley.
Sekar, V., Q. Jiang, C. Shu, and B. C. Khoo. 2019. “Fast flow field prediction over airfoils using deep learning approach.” Phys. Fluids 31 (5): 057103. https://doi.org/10.1063/1.5094943.
Tao, J., and G. Sun. 2019. “Application of deep learning based multi-fidelity surrogate model to robust aerodynamic design optimization.” Aerosp. Sci. Technol. 92 (7): 722–737. https://doi.org/10.1016/j.ast.2019.07.002.
Wen, L., L. Gao, and X. Li. 2017. “A new deep transfer learning based on sparse auto-encoder for fault diagnosis.” IEEE Trans. Syst. Man Cybern.: Syst. 49 (1): 136–144. https://doi.org/10.1109/TSMC.2017.2754287.
West, J., D. Ventura, and S. Warnick. 2007. Spring research presentation: A theoretical foundation for inductive transfer. Provo, UT: Brigham Young Univ.
Xueyi, L., L. Jialin, Q. Yongzhi, and H. David. 2020. “Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning.” Chin. J. Aeronaut. 33 (2): 418–426. https://doi.org/10.1016/j.cja.2019.04.018.
Yu, D., Z. Han, B. Zhang, M. Zhang, H. Liu, and Y. Chen. 2022. “A multi-autoencoder fusion network for fast image prediction of aircraft ice accretion.” Phys. Fluids 34 (7): 076107. https://doi.org/10.1063/5.0091068.
Zhang, Y., W. J. Sung, and D. N. Mavris. 2018. “Application of convolutional neural network to predict airfoil lift coefficient.” In Proc., 2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conf. Reston, VA: American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/6.2018-1903.
Zhao, Y.-P., and Y.-B. Chen. 2022. “Extreme learning machine based transfer learning for aero engine fault diagnosis.” Aerosp. Sci. Technol. 121 (Feb): 107311. https://doi.org/10.1016/j.ast.2021.107311.
Information & Authors
Information
Published In
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Apr 7, 2022
Accepted: Nov 23, 2022
Published online: Jul 31, 2023
Published in print: Nov 1, 2023
Discussion open until: Dec 31, 2023
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.