Fault detection and isolation of aircraft air data/inertial system

A method for failure detection and isolation (FDI) for redundant aircraft sensors is presented. The outputs of the concerned sensors are involved in the computation of §ight control laws, and the objective is to eliminate any fault before propagation in the control loop when selecting a unique §ight parameter among a set (generally, three) of redundant measurements. The particular case of an oscillatory failure is investigated. The proposed method allows an accurate FDI of erroneous sensor and computes a consolidated parameter based on the fusion of data from remaining valid sensors. The bene¦ts of the presented method are to enhance the data fusion process with FDI techniques which improve the performance of the fusion when only few sources (less than three) are initially valid.

[1]  H. Khalil,et al.  Discrete-time implementation of high-gain observers for numerical differentiation , 1999 .

[2]  Philippe Goupil.  AIRBUS State of the Art and Practices on FDI and FTC , 2009 .

[3]  Ron J. Patton,et al.  Robust Model-Based Fault Diagnosis: The State of the ART , 1994 .

[4]  Robert Babuska,et al.  Soft computing applications in aircraft sensor management and flight control law reconfiguration , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[5]  Rolf Isermann.  Model-based fault-detection and diagnosis - status and applications § , 2004 .

[6]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[7]  Eric Walter,et al.  A new model-free method performing closed-loop fault diagnosis for an aeronautical system , 2009 .

[8]  Graham C. Goodwin,et al.  Digital control and estimation : a unified approach , 1990 .

[9]  Philippe Goupil.  Oscillatory failure case detection in the A380 electrical flight control system by analytical redundancy , 2007 .

[10]  A. Zolghadri.  An algorithm for real-time failure detection in Kalman filters , 1996, IEEE Trans. Autom. Control..

[11]  Ali Zolghadri,et al.  A novel non-linear observer-based approach to oscillatory failure detection , 2009, 2009 European Control Conference (ECC).

[12]  Jeffrey W. Hegg.  Enhanced space integrated GPS/INS (SIGI) , 2002 .

[13]  Michèle Basseville,et al.  Detection of abrupt changes: theory and application , 1993 .

[14]  Huamin Jia.  Data fusion methodologies for multisensor aircraft navigation systems , 2004 .

[15]  Stephen Osder,et al.  Practical View of Redundancy Management Application and Theory , 1999 .

[16]  Artem Kremlev,et al.  Identification of frequency of biased harmonic signal , 2007, ALCOSP.

[17]  Ali Zolghadri,et al.  Fault detection and isolation for redundant aircraft sensors , 2010, 2010 Conference on Control and Fault-Tolerant Systems (SysTol).

[18]  Artem Kremlev,et al.  Identification of Frequency of Biased Harmonic Signal , 2007, Eur. J. Control.