DETECTING AND IDENTIFYING MULTIPLE FAILURES IN A FLIGHT CONTROL SYSTEM

Parity space and parameter estimation methods are combined to detect and identify sensor and actuator failures in a control system. Parity residuals (or error indicators) are generated from analytical relations describing the dynamics of the system. Representing the failures in sensors and actuators as biases and changes in scale, a recursive least-squares estimator is designed to identify the source and magnitude of all failures from the information contained in the residuals. The choice of sensors, length of the information window, and possible combinations of sensors and actuators on the performance of the failure detection and identification procedure are addressed. Simulation results on a linear aircraft model with wind disturbances and sensor noise show the validity of the present approach.

[1]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results , 1990, Autom..

[2]  Marc Bodson,et al.  Multivariable adaptive algorithms for reconfigurable flight control , 1997, IEEE Trans. Control. Syst. Technol..

[3]  Peter S. Maybeck,et al.  Sensor/actuator failure detection in the Vista F-16 by multiple model adaptive estimation , 1995 .

[4]  Mohammad-Ali Massoumnia,et al.  Generating parity relations for detecting and identifying control system component failures , 1988 .

[5]  Rolf Isermann,et al.  Process Fault Detection Based on Modeling and Estimation Methods , 1982 .

[6]  Robert F. Stengel,et al.  A rule-based paradigm for intelligent adaptive flight control , 1989 .

[7]  Robert F. Stengel,et al.  A fuzzy logic-parity space approach to actuator failure detection and identification , 1998 .

[8]  Paul M. Frank,et al.  Fault diagnosis in dynamic systems: theory and application , 1989 .

[9]  Yoshimasa Ochi.  APPLICATION OF FEEDBACK LINEARIZATION METHOD IN A DIGITAL RESTRUCTURABLE FLIGHT CONTROL SYSTEM , 1993 .

[10]  George C. Verghese,et al.  Optimally robust redundancy relations for failure detection in uncertain systems , 1986, Autom..

[11]  Jason L. Speyer,et al.  Robust fault detection filter design , 1995, Proceedings of 1995 American Control Conference - ACC'95.

[12]  Robert F. Stengel,et al.  Optimal Control and Estimation , 1994 .

[13]  Janos Gertler,et al.  A new structural framework for parity equation-based failure detection and isolation , 1990, Autom..

[14]  A. Willsky,et al.  Analytical redundancy and the design of robust failure detection systems , 1984 .

[15]  Michèle Basseville,et al.  Detection of Abrupt Changes in Signals and Dynamical Systems , 1985 .

[16]  Rolf Isermann,et al.  Fault diagnosis of machines via parameter estimation and knowledge processing - Tutorial paper , 1991, Autom..

[17]  M FrankPaul.  Fault diagnosis in dynamic systems using analytical and knowledge-based redundancya survey and some new results , 1990 .

[18]  BLOCKED STATIC PORTS EYED IN AEROPERU 757 CRASH , 1996 .

[19]  David G. Ward,et al.  IMPLEMENTATION AND FLIGHT TEST ASSESSMENT OF AN ADAPTIVE, RECONFIGURABLE FLIGHT CONTROL SYSTEM , 1997 .

[20]  John E. White,et al.  Detection Filter Design: Spectral Theory and Algorithms , 1986, 1986 American Control Conference.

[21]  J. Chen,et al.  A Review of Parity Space Approaches to Fault Diagnosis , 1991 .

[22]  Rolf Isermann,et al.  Process fault detection based on modeling and estimation methods - A survey , 1984, Autom..