Abstract
This chapter is dedicated to techniques for ensuring fault tolerance in redundant aircraft sensors involved in computation of flight control laws. The objective is to switch off the faulty sensor and to compute a reliable (a.k.a. as “consolidated”) parameter using data from valid sensors, in order to eliminate any anomaly before propagation in the control loop. The benefit of the presented method is to improve the consolidation process with a fault detection and isolation approach when only few sources (less than three) are valid. Different techniques are compared to accurately detect any behavioral change of the sensor outputs. The approach is tested on a recorded flight dataset. This chapter is dedicated to fault detection and isolation of redundant aircraft sensors involved in the computation of flight control laws. The objective is to switch off the erroneous sensor and to compute a so-called consolidated parameter using data from valid sensors, in order to eliminate any anomaly before propagation in the control loop. We will focus on oscillatory failures and present a method for integrity control based on the processing of any flight parameter measurement in the flight control computer (FCC) like, e.g., anemometric and inertial data. One of the main tasks dedicated to the FCC is the flight control laws (FCL) computation which generates a command (position order) to servo-control each moving surface (see Fig. 5.1). The comparison between the pilot commands (or the piloting objectives) and the aircraft state is used for FCL computation. The aircraft state is measured by a set of sensors delivering, e.g., anemometric and inertial measurements that characterize the aircraft attitude, speed, and altitude. The data is acquired using an acquisition system composed by several dedicated redundant units (usually three). The FCC receives three redundant values of each flight parameter data from the sensors and must compute unique and valid flight parameters required for the FCL computation. This specific data fusion processing, called “consolidation,” classically consists of two simultaneous steps (Fig. 5.2): selection or computation of one unique parameter from the three available sources, and, in parallel, monitoring of each of the three independent sources to discard any faulty one. As a consequence, the consolidation allows reliable flight parameters computation with the required accuracy by discarding any involved failed source.
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Notes
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Innovative and robust strategies for spacecraft autonomy.
References
Steffen T (2005) Control reconfiguration of dynamical systems: linear approaches and structural tests, Lectures notes in control and information sciences. Springer, Berlin
Allerton D, Jia H (2008) Distributed data fusion algorithms for inertial network systems. Radar Sonar Navig IET 2(1):51–62
Oosterom M, Babuska R, Verbruggen H (2002) Soft computing applications in aircraft sensor management and flight control law reconfiguration. IEEE Trans Syst Man Cybern Part C Appl Rev 32(2):125–139
Hegg J (2002) Enhanced space integrated GPS/INS (SIGI). IEEE Aerosp Electron Syst Mag 17(4):26–33
Kerr TH (1987) Decentralized filtering and redundancy management for multisensor navigation. IEEE Trans Aerosp Electron Syst AES-23(1):83–119
Lawrence P, Berarducci M (1996) Navigation sensor, filter, and failure mode simulation results using the distributed Kalman filter simulator (DKFSIM). In: Proceedings of IEEE PLANS, Atlanta, Georgia, April 22–26, pp 697–710
Rao B, Durrant-Whyte H (1991) Fully decentralised algorithm for multisensory Kalman filtering. IEEE Proc Control Theory Appl 138(5):413–420
Tupysev V (2000) Federated Kalman filter via formation of relation equations in augmented state space. J Guid Control Dyn 23(3):391–398
Boskovic JD, Mehra RK (2002) Failure detection, identification and reconfiguration in flight control. In: Fault diagnosis and fault tolerance for mechatronic systems: recent advances. Springer, Berlin
Alcorta-garcia E, Zolghadri A, Goupil P (2011) A nonlinear observer-based strategy for aircraft oscillatory failure detection: A380 case study. IEEE Trans Aerosp Electron Syst 47:2792–2806
Goupil P (2010) Oscillatory failure case detection in the A380 electrical flight control system by analytical redundancy. Control Eng Pract 18(9):1110–1119
Berdjag D, Cieslak J, Zolghadri A (2012) Fault diagnosis and monitoring of oscillatory failure case in aircraft inertial system. Control Eng Pract 20(12):1410–1425
Hagglund T (1995) A control loop performance monitor. Control Eng Pract 3(11):1543–1551
Thornhill NF, Hagglund T (1997) Detection and diagnosis of oscillation in control-loop. Control Eng Pract 5(10):1343–1354
Miao T, Seborg DE (1999) Automatic detection of excessively oscillatory feedback in control loops. In: IEEE international conference on control applications, Hawaii, pp 359–364
Thornhill NF, Huang B, Zhang H (2003) Detection of multiple oscillations in control loops. J Process Control 13(1):91–100
Harris TJ, Seppala CT, Desborough LD (1999) A review of performance monitoring and assessment techniques for univariate and multivariate control systems. J Process Control 9(1):1–17
Xia C, Howell J (2003) Loop status monitoring and fault localization. J Process Control 13(7):679–691
Shoukat Choudhury MAA, Shah SL, Thornhill NF (2004) Diagnosis of poor control-loop performance using higher-order statistics. Automatica 40:1719–1728
Kay S, Gabriel J (2002) Optimal invariant detection of a sinusoid with unknown parameters. IEEE Trans Signal Process 50(1):27–40
Yang ZY, Chan CW, Mok HT (2006) An approach to detect and isolate faults for nonlinear systems with periodic input. In: Proceedings of the SAFEPROCESS’06 conference, no. 1. Beijing, PR China, pp 301–306
Zivanovic M (2011) Detection of non-stationary sinusoids by using joint frequency reassignment and null-to-null bandwidth. Digit Signal Process 21(1):77–86
Odgaard PF, Wickerhauser MV (2007) Karhunen–Loeve based detection of multiple oscillations in multiple measurement signals from large-scale process plants. In: American control conference, New York City, USA, pp 5893–5898
Xia C, Howell J (2005) Isolating multiple sources of plant-wide oscillations via independent component. Control Eng Pract 13:1027–1035
Wilhelm L, Proetzsch M, Berns K (2009) Oscillation analysis in behavior-based robot architectures. In: Autonome mobile system. Springer, Berlin, p 121
Aranovskii SV, Bobtsov AA, Kremlev AS, Luk’yanova GV (2007) A robust algorithm for identification of the frequency of a sinusoidal signal. J Comput Syst Sci Int 46(3):371–376
Osder S (1999) Practical view of redundancy management application and theory. J Guid Control Dyn 22(1):12–21
Goupil P (2009) AIRBUS State of the art and practices on FDI and FTC. In: Proceedings of the 7th IFAC symposium on fault detection, supervision and safety of technical processes, Barcelona, Spain, July, pp 564–572
Odgaard PF, Trangbaek K (2006) Comparison of methods for oscillation detection – case study on a coal-fired power plant. In: Proceedings of the 5th IFAC symposium on power plants and power systems control, vol 5, Kananaskis, Canada, pp 297–302
Middleton R, Goodwin G (1990) Digital control and estimation. Prentice Hall, Inc., Englewood Cliffs
Zolghadri A (1996) An algorithm for real-time failure detection in Kalman filters. IEEE Trans Autom Control 41(10):1537–1539
Patton RJ (1994) Robust model-based fault diagnosis: the state of the art. In: Proceedings IFAC symposium SAFEPROCESS’94, vol. 1, Espoo, Finland, pp 1–24
Isermann R (2005) Model-based fault detection and analysis – status and application. Annu Rev Control 29:71–85
Frank P, Ding SX (1997) Survey of robust residual generation and evaluation methods in observer-based fault detection systems. J Process Control 7(6):403–424
Basseville M, Nikiforov IV (1993) Detection of abrupt changes – theory and application. Prentice-Hall, Inc., Englewood Cliffs
Marzat J, Piet-Lahanier H, Damongeot F, Walter E (2009) A new model-free method performing closed-loop fault diagnosis for an aeronautical system. In: 7th workshop on advanced control and diagnosis, ACD’2009. Zielona Gora, Poland. http://www.issi.uz.zgora.pl/ACD_2009/program/Papers/06_ACD_2009.pdfS
Dabroom A, Khalil H (1999) Discrete-time implementation of high-gain observers for numerical differentiation. Int J Control 72(17):1523–1537
Berdjag D, Zolghadri A, Cieslak J, Goupil P (2010) Fault detection and isolation for redundant aircraft sensors. In: Proceedings of the conference on control and fault-tolerant systems (SysTol’10), Nice, France, October, pp 137–142
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Zolghadri, A., Henry, D., Cieslak, J., Efimov, D., Goupil, P. (2014). Failure Detection and Compensation for Aircraft Inertial System. In: Fault Diagnosis and Fault-Tolerant Control and Guidance for Aerospace Vehicles. Advances in Industrial Control. Springer, London. https://doi.org/10.1007/978-1-4471-5313-9_5
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DOI: https://doi.org/10.1007/978-1-4471-5313-9_5
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