Pitot Tube Fault Warning Method Based on Fully Connected Neural Networks
Source abstract
Abstract
The pitot tube is the core sensor for aircraft to obtain external atmospheric data, and its failure has a very important impact on flight safety. However, as its structure and principle are relatively simple, all manufacturers have not adopted available monitoring methods for its health status due to the perspective of cost and complexity reduction. The pitot tube fault warning method is conducted in this paper with a fully connected neural network (FCNN) method based on the data collected by the pitot tube itself. By constructing and selecting parameters and extracting fault features from flight record data, a pitot tube fault warning model based on an FCNN is constructed. The effectiveness of the proposed method is verified through pitot tube fault warning experiments based on actual flight record data, which can provide technical reference for pitot tube fault warning during aircraft route operation in the future.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fault Detection and Control Systems · Advanced Sensor Technologies Research · Aerospace and Aviation Technology
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
China Aero-Polytechnology Establishment, Beijing 100028, China
The School of Civil Aviation, Northwestern Polytechnical University, Xi’an 710072, China
Funding
National Nature Science Foundation of China · Shaanxi Province
References (29)
Show source-provided references
1. Analysis of Air France 447 Accident Based on Cream Model
2. Analysis of Pitot Tube Anti-icing Fault of Airbus A320 Aircraft
3. Analysis of Partial Failure Fault of Unwarned Pitot Tube Heater for Boeing 737NG Aircraft
4. Li, Z., Ma, J., Fan, R., Zhao, Y., Ai, J., and Dong, Y. (2025). Aircraft sensor fault diagnosis based on graphsage and attention mechanism. Sensors, 25.10.3390/s25030809 ↗
5. Larocque, F., De Ponti, T., Remes, B., and Smeur, E. (2025, January 6–10). Synthetic Air Data System for Pitot Tube Failure Detection on the Variable Skew Quad Plane. Proceedings of the AIAA SCITECH 2025 Forum, Orlando, FL, USA.10.2514/6.2025-2609 ↗
6. Sun, K., and Gebre-Egziabher, D. (2020). A fault detection and isolation design for a dual pitot tube air data system. 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS), IEEE.10.1109/PLANS46316.2020.9110179 ↗
7. Meng, X., Zhang, Z., Li, J., Jing, Z., and Zhou, W. (2025). Research on Typical Fault Diagnosis of Fixed-Wing Unmanned Aerial Vehicles Based on CNN-LSTM-Attention Model. 2025 International Conference on Unmanned Systems and Technology (UST), IEEE.10.1109/UST66533.2025.11340387 ↗
8. Data-driven schemes for robust fault detection of air data system sensors10.1109/TCST.2017.2758345 ↗
9. Detection and warning of ice crystals clogging pitot probes from total air temperature anomalies10.1016/j.ast.2020.105874 ↗
10. An application of deep neural networks to the in-flight parameter identification for detection and characterization of aircraft icing10.1016/j.ast.2018.02.026 ↗
11. A Data-Driven Integrated Safety Risk Warning Model Based on Deep Learning for Civil Aircraft
12. Air Data Sensor Fault Detection with an Augmented Floating Limiter10.1155/2018/1072056 ↗
13. A cumulative exposure model for remaining useful life prediction of pitot tube based on dynamic operating data10.1002/qre.3299 ↗
14. Observability and performance analysis of a model-free synthetic air data estimator10.2514/1.C035290 ↗
15. Hajiyev, C., Cilden-Guler, D., and Hacizade, U. (2019). EKF for Wind Speed Estimation and Sensor Fault Detection Using Pitot Tube Measurements. 2019 9th International Conference on Recent Advances in Space Technologies (RAST), IEEE.10.1109/RAST.2019.8767902 ↗
16. Channel attention & temporal attention based temporal convolutional network: A dual attention framework for remaining useful life prediction of the aircraft engines10.1016/j.aei.2024.102372 ↗
17. A multi-channel data-based fault diagnosis method integrating deep learning strategy for aircraft sensor system10.1088/1361-6501/aca219 ↗
18. A New Early Fault Warning Method for Airborne Lithium Battery Packs
19. Spatio-temporal graph attention network-based fault warning of coal mill
20. Fault Warning and Location in Battery Energy Storage Systems via Venting Acoustic Signal10.1109/JESTPE.2021.3101151 ↗
21. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks10.1109/TGRS.2016.2584107 ↗
22. Memari, M., Shekaramiz, M., Masoum, M.A.S., and Seibi, A.C. (2024). Data fusion and ensemble learning for advanced anomaly detection using multi-spectral RGB and thermal imaging of small wind turbine blades. Energies, 17.10.3390/en17030673 ↗
23. Fault diagnosis in an asynchronous motor using three-dimensional convolutional neural network10.1007/s13369-023-08025-y ↗
24. Trees, forests, and impurity-based variable importance in regression
25. Mean decrease accuracy for random forests: Inconsistency, and a practical solution via the Sobol-MDA10.1093/biomet/asac017 ↗
26. Big Data Statistical Trend Analysis and PCA for Early Fault Diagnosis of Rolling Bearings
27. Learning representations by back-propagating errors10.1038/323533a0 ↗
28. Nair, V., and Hinton, G.E. (2010, January 21–24). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel.
29. Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. Comput. Sci.