Developing an Approach for Fault Detection and Diagnosis of Angular Velocity Sensors

Year 2021, Volume: 02 Issue: 01, 15 - 21, 27.06.2021

Hong Son Tran , Thi Thuy Tran Dinh-dung Nguyen , Dat Dang Quoc Hong Tien Nguyen

https://doi.org/10.23890/IJAST.vm02is01.0102

Abstract

Angular velocity sensor detection and diagnosis become increasingly essential for the improvement of reliability, safety, and efficiency of the control system on aircraft. The classical methods for fault detection and diagnosis are limit or trend checking of some measurable output variables. Due to they do not give a deeper insight and usually do not allow a fault diagnosis, model-based methods of fault detection and diagnosis were developed by using input and output signals and applying dynamic process models. These approaches are based on parameter estimation, parity equations, or state observers. This paper presents an improvement method to build algorithm fault diagnosis for angular velocity sensors on aircraft. Based on proposed method, results of paper can be used in designed intelligent systems that can automatically fault detection on aircraft.

Keywords

Block sensor;, Fault diagnosis, Fault detection, Angular velocity sensor, Advanced control system

References

• Xue, W., Guo, Y.Q. and Zhang, X.D., 2007, September. A bank of Kalman filters and a robust Kalman filter applied in fault diagnosis of aircraft engine sensor/actuator. In Second International Conference on Innovative Computing, Informatio and Control (ICICIC 2007) (pp. 10-10). IEEE.
• He, Q., Zhang, W., Lu, P. and Liu, J., 2020, Performance comparison of representative model-based fault reconstruction algorithms for aircraft sensor fault detection and diagnosis. Aerospace Science and Technology, 98, p.105649.
• Chen, J. and Patton, R.J., 2012, Robust model-based fault diagnosis for dynamic systems (Vol. 3). Springer Science & Business Media. Isermann, R., 2005. Model-based fault-detection and diagnosis–status and applications. Annual Reviews in control, 29(1), pp.71-85.
• Lu, P., Van Eykeren, L., Van Kampen, E., De Visser, C.C. and Chu, Q.P., 2016, Adaptive three-step kalman filter for air data sensor fault detection and diagnosis. Journal of Guidance, Control, and Dynamics, 39(3), pp.590-604.
• Lu, P., Van Eykeren, L., van Kampen, E.J., de Visser, C. and Chu, Q., 2015, Double-model adaptive fault detection and diagnosis applied to real flight data. Control Engineering Practice, 36, pp.39-57.
• 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.
• Xue, W., Guo, Y.Q. and Zhang, X.D., 2007, September. A bank of Kalman filters and a robust Kalman filter applied in fault diagnosis of aircraft engine sensor/actuator. In Second International Conference on Innovative Computing, Information and Control (ICICIC 2007) (pp. 10-10). IEEE.
• Baskaya, E., Bronz, M. and Delahaye, D., 2017, September. Fault detection & diagnosis for small UAVs via machine learning. In 2017 IEEE/AIAA 36th Digital Avionics Systems Conference (DASC) (pp. 1-6). IEEE.
• Hajiyev, C. and Soken, H.E., 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.
• Tuan, D. Q., Firsov, S.N. and Pishchukhina, O.A. “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, 2012, Vol. 11, issue 2, pp. 84-88.