Journal of the Korean Society for Aeronautical & Space Sciences (한국항공우주학회지)

The Korean Society for Aeronautical and Space Sciences (한국항공우주학회)
권호 표지
DOI QR코드

DOI QR Code

Real Time Fault Diagnosis of UAV Engine Using IMM Filter and Generalized Likelihood Ratio Test

IMM 필터 및 GLRT를 이용한 무인기용 엔진의 실시간 결함 진단

  • Received : 2022.05.10
  • Accepted : 2022.06.13
  • Published : 2022.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

An effective real time fault diagnosis approach for UAV engine is drawn from IMM filter and GLRT methods. For this purpose based on the linear diagnosis model derived from engine dynamic performance analysis the Kalman filter for residual estimation and each method are applied to the fault diagosis of the actuator for engine control sensors. From the process of the IMM filter application the effective FDI measure is obtained and the state responses due to actuator fault are estimated. Likewise from the GLRT method the fault magnitudes of actuator and sensors are estimated associated with some FDI functionings. The numerical simulations verify the effectiveness of the IMM filter for FDI and the GLRT in estimating the fault magnitudes of each fault mode.

IMM 필터 및 GLRT 기법을 이용하여 무인기용 엔진의 효과적인 실시간 결함 진단 방안을 도출하였다. 이를 위해서 엔진 동적 사이클해석으로부터 선형 진단 모델을 유도하고 잔차 추정을 위한 칼만필터를 도입한 후 각 기법의 특성을 고찰하여 엔진 제어 구동기 및 센서의 결함 진단에 적용하였다. 이 과정에서 IMM 필터로부터 효과적인 FDI 방안을 도출하였고 구동기 결함으로 인한 상태변수의 반응값을 추정하였으며, GLRT로부터는 구동기 및 센서의 결함값 추정과 FDI 기능을 확인하였다. 수치 모의시험 결과를 통해서 FDI를 위한 IMM 필터의 효용성과 각 결함 모드의 결함값 추정을 위한 GLRT 기법의 효용성을 확인하였다.

Keywords

Acknowledgement

본 연구는 방위사업청, 국방과학연구소의 "터보팬 엔진의 건전성 관리를 위한 탑재형 소프트웨어 개발" 과제의 일환으로 수행되었습니다.

References

  1. https://en.wikipedia.org/wiki/FADEC
  2. Borquet, S. and Leonard, O., "A Generalized Likelihood Ratio Test for Adaptive Gas Turbine Performance Monitoring," Journal of Engineering for Gas Turbines and Power, Vol. 131, No. 1, January 2009, pp. 011601-1~608. https://doi.org/10.1115/1.2967493
  3. Meskin, N., Pourbabaee, B. and Khorasani, K., "Multiple-model based sensor fault diagnosis using hybrid kalman filter approach for nonlinear gas turbine engines," Proceedings of the American Control Conference, June 2013, pp. 4717~4723.
  4. Yang, Q., Li, S. and Cao, Y., "An IMM-GLR Approach for Marine Gas Turbine Gas Path Fault Diagnosis," Mathematical Problems in Engineering, Vol. 2018, September 2018, pp. 1~12.
  5. Han, D. J., "A Study on Real Time Fault Diagnosis and Health Estimation of Turbojet Engine through Gas Path Analysis," International Journal of Aeronautical and Space Sciences, Vol. 49, No. 4, 2021, pp. 311~320.
  6. Blom, H., Chang, K. C. and Bar-Shanlom, Y., "Tracking a Maneuvering Target Using Input Estimation Versus the Interacting Multiple Model Algorithm," IEEE Transactions on Aerospace and Electronic Systems, Vol. 25, No. 2, March 1989, pp. 296~300. https://doi.org/10.1109/7.18693
  7. Zhang, Y. and Li, X. R., "Detection and Diagnosis of Sensor and Actuator Failures Using IMM Estimator," IEEE Transactions on Aerospace and Electronic Systems, Vol. 34, No. 4, October 1998, pp. 1293~1313. https://doi.org/10.1109/7.722715
  8. Kim, B. D. and Lee, J. S., "Steady State Kalman Filter for Multi-Target Tracking," International Journal of Aeronautical and Space Sciences, Vol. 34, No. 8, 2006, pp. 71~78.
  9. Yang, C., Mohammadi, A. and Chen, Q. W., "Multi-Sensor Fusion with Interaction Multiple Model and Chi-Square Test Tolerant Filter," Sensors, Vol. 16, No. 11, November 2016, pp. 1835~1850. https://doi.org/10.3390/s16111835
  10. Willsky, A. and Jones, H., "A Generalized Likelihood Ratio Approach to the Detection and Estimation of Jumps in Linear Systems," IEEE Transactions on Automatic Control, February 1976, pp. 108~112.
  11. Patwardhan, S. C., Manuja, S., Narasimhan, S. and Shah, S. L., "From data to diagnosis and control using generalized orthonormal basis filters. Part II: Model predictive and fault tolerant control," Journal of Process Control, Vol. 16, Iss. 2, April 2013, pp. 157~175.
  12. Hanlon, P. D. and Maybeck, P. S., "Multiple Model Adaptive Estimation Using a Residual Correlation Kalman Filter Bank," IEEE Transactions on Aerospace and Electronic Systems, Vol. 36, No. 2, April 2000, pp. 393~406. https://doi.org/10.1109/7.845216
  13. Geyser, L. C., "DYABCD A Program for Calculating Linear A,B,C,D Matrices from a Nonlinear Dynamic Engine Simulation," NASA TP-1295, 1978.
  14. Simon, D. L., "Optimal State Estimation," Wiley-Interscience, 2006.