Structural Analysis Approach for the Generation of Structured Residuals for Aircraft FDI

A systematic methodology is described for calculating structured residuals with high fault diagnostic capabilities for detecting sensor and actuators failures. The effort addresses implementation issues for real-time applications such as residual computation complexity and sensitivity to measurement noise. These specific requirements have been rigorously introduced through a cost function measuring the quality of the residual signal. A structural analysis approach of the nonlinear model of the system in conjunction with the unknown variables elimination method is used to derive subsets of residual equations. An algorithm is proposed for selecting the residual equations with maximum "failure isolability" and minimum cost, according to the selected performance criteria. The methodology has been applied to the design of a real-time residual generator for a nonlinear model of a remotely controlled semi-scale YF-22 research aircraft.

[1]  Erik Frisk,et al.  Lowering orders of derivatives in non-linear residual generation using realization theory , 2005, Autom..

[2]  J. Burken,et al.  Design of safety monitor schemes for a fault tolerant flight control system , 2006, IEEE Transactions on Aerospace and Electronic Systems.

[3]  Giampiero Campa,et al.  On‐line learning neural networks for sensor validation for the flight control system of a B777 research scale model , 2002 .

[4]  Youmin Zhang,et al.  Detection and diagnosis of sensor and actuator failures using IMM estimator , 1998 .

[5]  Linda Rattfält.  A comparative study of two structural methods for fault isolation analysis , 2004 .

[6]  Marc Rauw,et al.  FDC 1.2 - A Simulink Toolbox for Flight Dynamics and Control Analysis , 2001 .

[7]  Alberto Isidori,et al.  A Geometric Approach to Nonlinear Fault Detection and Isolation , 2000 .

[8]  Marcel Staroswiecki,et al.  Structural Analysis of Fault Isolability in the DAMADICS benchmark , 2006 .

[9]  Mogens Blanke,et al.  Cheap diagnosis using structural modelling and fuzzy-logic-based detection , 2003 .

[10]  Erik Frisk,et al.  Residual Generator Design for Non-linear, Polynomial Systems - A Gröbner Basis Approach , 2000 .

[11]  Mattias Krysander,et al.  An Efficient Algorithm for Finding Over-constrained Sub-systems for Construction of Diagnostic Tests , 2005 .

[12]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[13]  Paul M. Frank,et al.  Fault diagnostics and fault tolerant control , 1998 .

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

[15]  M. Staroswiecki,et al.  A Structural Framework for the Design of FDI System in Large Scale Industrial Plants , 2000 .

[16]  Frank L. Lewis,et al.  Aircraft Control and Simulation , 1992 .

[17]  J. Gertler.  Fault detection and isolation using parity relations , 1997 .

[18]  J. Gauthier,et al.  Erratum Observability and Observers for Nonlinear Systems , 1995 .

[19]  Michèle Basseville,et al.  Fault Detection and Isolation in Nonlinear Dynamic Systems: A Combined Input-Output and Local Approach , 1998, Autom..

[20]  Marcello R. Napolitano,et al.  Design and flight-testing of non-linear formation control laws , 2007 .

[21]  V. Cocquempot,et al.  Structural analysis for residual generation: towards implementation , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..

[22]  Marcel Staroswiecki,et al.  Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems , 2001, Autom..

[23]  Marcel Staroswiecki,et al.  Extension of Parity Space to Non Linear Polynomial Dynamic Systems , 1997 .

[24]  Ron J. Patton,et al.  Fault detection and diagnosis in aerospace systems using analytical redundancy , 1991 .