Volume – 3 Issue – 2 Article – 5

Improving the Efficiency of Angular Velocity Sensors on Aircraft

Trung Vuong Anh1, Hong Son Tran2, Dinh-dung Nguyen3, Truong-thanh Nguyen4 Omar Alharasees5, Utku Kale6

1 Faculty of Aviation Technical, Air defense-Air Force Academy, 100000 Hanoi, Vietnam
2 Faculty of Control Engineering, Le Quy Don Technical University, 100000 Hanoi, Vietnam,
3 Department of Aircraft System Design, Faculty of Aerospace Engineering, Le Quy Don Technical University, 100000 Hanoi, Vietnam
4 Department of Aeronautics and Naval Architecture, Budapest University of Technology and Economics, Pest, Hungary,
5 Department of Aircraft-Engines, Air Force Officer’s College, 650000 Khanh Hoa, Vietnam
6 Department of Aeronautics, Budapest University of Technology and Economics, Pest, Hungary

F IJAST 2022; 3 (2) DOI: 10.23890/IJAST.vm03is02.0205; Language: EN

With the development of science and technology, intelligent systems on
aircraft help users know the device’s operating status in real time. Using smart
devices shortens the time for maintenance, repair, and operation of ground
equipment and aircraft equipment. Therefore, building devices capable of
self-diagnosis and warning failure are essential in aeronautical engineering. In
many published studies, the authors often use the foundation of classic
algorithms such as genetics, neural networks, and AI to solve the problem of
identification and troubleshoot some simple devices. In Vietnam, there are
currently not many published studies on failure diagnosis in aviation
engineering, so the author’s research has built the foundation for developing
studies on fault diagnosis crash in the future. The primary purpose of the
research is to create a complete automatic fault diagnosis and repair system
for a specific class of inductance (angular speed sensor). The algorithms
proposed in the paper are simulated on Matlab Simulink software, which will
prove the feasibility of the proposed algorithm. In future studies, the author
will apply new algorithms to build more complex fault diagnosis systems for
other objects on the flying device.

Angular velocity sensor
Fault detection
Fault diagnosis

  1. References
    Arockiam, N.J., Jawaid, M. and Saba, N. (2018) ‘Sustainable bio composites for aircraft components’, in Jawaid, M. and Thariq, M.B.T.-S.C. for AA (eds) Woodhead Publishing Series in Composites Science and Engineering. Woodhead Publishing, pp. 109–123. doi:10.1016/B978-0-08-102131-6.00006-2.
  2. Baskaya, E., Bronz, M. and Delahaye, D. (2017) ‘Fault detection & diagnosis for small UAVs via machine learning’, AIAA/IEEE Digital Avionics Systems Conference – Proceedings, 2017-Septe. doi:10.1109/DASC.2017.8102037.
  3. Brooks, S. and Roy, R. (2021) ‘An overview of selfengineering systems’, https://doi.org/10.1080/09544828.2021.1914323,32(8), pp. 397–447. doi:10.1080/09544828.2021.1914323.
  4. Cardei, M. and Du, D.Z. (2005) ‘Improving Wireless Sensor Network Lifetime through Power Aware Organization’, Wireless Networks 2005 11:3, 11(3), pp. 333–340. doi:10.1007/S11276-005-6615-6.
  5. Chen, W. Bin et al. (2009) ‘Knowledge base design for fault diagnosis expert system based on production rule’, Proceedings – 2009 Asia-Pacific Conference on Information Processing, APCIP 2009, 1, pp. 117–119. doi:10.1109/APCIP.2009.38.
  6. CHU, L. et al. (2022) ‘Design, modeling, and control of morphing aircraft: A review’, Chinese Journal of Aeronautics, 35(5), pp. 220–246. doi:10.1016/J.CJA.2021.09.013.
  7. Hajiyev, C. and Caliskan, F. (2000) ‘Sensor/actuator fault diagnosis based on statistical analysis of innovation sequence and Robust Kalman Filtering’, Aerospace Science and Technology, 4(6), pp. 415–422. doi:10.1016/S1270-9638(00)00143-7.
  8. Hajiyev, C. and Caliskan, F. (2005) ‘Sensor and control surface/actuator failure detection and isolation applied to F-16 flight dynamic’, Aircraft Engineering and Aerospace Technology, 77(2), pp. 152–160. doi:10.1108/00022660510585992/FULL/PDF.
  9. Hajiyev, C. and Soken, HE (2013) ‘Robust Adaptive Kalman Filter for estimation of UAV dynamics in the presence of sensor/actuator faults’, Aerospace Science and Technology, 28(1), pp. 376–383. doi:10.1016/j.ast.2012.12.003.
  10. He, Q. et al. (2020) ‘Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis’, Aerospace Science and Technology, 98, p.105649. doi:10.1016/j.ast.2019.105649.
  11. Iemma, U., Pisi Vitagliano, F. and Centracchio, F. (2018) ‘A multi-objective design optimisation of eco-friendly aircraft: the impact of noise fees on airplanes sustainable development’, International Journal of Sustainable Engineering, 11(2), pp. 122–134. doi:10.1080/19397038.2017.1420109.
  12. Isermann, R. (2005) Fault-diagnosis systems: an introduction from fault detection to fault tolerance. Springer Science & Business Media.
  13. Khamis, A. (2021) ‘Smart Mobility’, Smart Mobility [Preprint]. doi:10.1007/978-1-4842-7101-8.
  14. Kim, S., Choi, J. and Kim, Y. (2008) ‘Fault detection and diagnosis of aircraft actuators using fuzzy-tuning IMM filter’, IEEE Transactions on Aerospace and Electronic Systems, 44(3), pp. 940–952. doi:10.1109/TAES.2008.4655354.
  15. Lu, P et al. (2015) ‘Adaptive Three-Step Kalman Filter for Air Data Sensor Fault Detection and Diagnosis’, Journal of Guidance, Control, and Dynamics, 39(3), pp. 590–604. doi:10.2514/1.G001313.
  16. Lu, Peng et al. (2015) ‘Double-model adaptive fault detection and diagnosis applied to real flight data’, Control Engineering Practice, 36, pp. 39–57. doi:10.1016/j.conengprac.2014.12.007.
  17. Erhan, İ., Kapanoğlu, M. and Karakoç, T. (2011) ‘Concurrent aircraft routing and maintenance scheduling ’, JOURNAL OF AERONAUTICS AND SPACE TECHNOLOGIES, 5(1), pp. 73–79.
  18. Sullivan, G.F. (1988) ‘A O(t3+ |E| ) Fault Identification Algorithm for Diagnosable Systems’, IEEE Transactions on Computers, 37(4), pp. 388–397. doi:10.1109/12.2182.
  19. Tran, H.S. et al. (2021) ‘Developing An Approach For Fault Detection And Diagnosis Of Angular Velocity Sensors’, International Journal of Aviation Science and Technology, 2(1), pp. 15–21. doi:10.23890/IJAST.vm02is01.0102.
  20. Tuan, Q.D., Firsov, S.N. and Pishchukhina, O.. (2012)‘Design a fault diagnose block of angular velocity sensors for control systems of a multipurpose aircraft’, Science and Technology of the Air Force of Ukraine, 2(11), pp. 84–88.
  21. Vieira, D.R. and Bravo, A. (2016) ‘Life cycle carbon emissions assessment using an eco-demonstrator aircraft: the case of an ecological wing design’, Journal of Cleaner Production, 124, pp. 246–257. doi:10.1016/j.jclepro.2016.02.089.
  22. Wang, Z., Zarader, J.. and Argentieri, S. (2012) ‘Aircraft fault diagnosis and decision system based on improved artificial neural networks’, in IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, pp. 1123–1128.
  23. Xue, W., Guo, Y.Q. and Zhang, X.D. (2007) ‘A bank of kalman filters and a Robust Kalman filter applied in fault diagnosis of aircraft engine sensor/actuator’, Second International Conference on Innovative Computing, Information and Control, ICICIC 2007 [Preprint]. doi:10.1109/ICICIC.2007.3.
  24. Yang, T. (2021) ‘Aviation Sensors and Their Calibration’, Telemetry Theory and Methods in Flight Test, pp. 81–149. doi:10.1007/978-981-33-4737-3_3.
Scroll to Top