Tracy-Widom distribution based fault detection approach: application to aircraft sensor/actuator fault detection.

The fault detection approach based on the Tracy-Widom distribution is presented and applied to the aircraft flight control system. An operative method of testing the innovation covariance of the Kalman filter is proposed. The maximal eigenvalue of the random Wishart matrix is used as the monitoring statistic, and the testing problem is reduced to determine the asymptotics for the largest eigenvalue of the Wishart matrix. As a result, an algorithm for testing the innovation covariance based on the Tracy-Widom distribution is proposed. In the simulations, the longitudinal and lateral dynamics of the F-16 aircraft model is considered, and detection of sensor and control surface faults in the flight control system which affect the innovation covariance, are examined.

[1]  Youmin Zhang,et al.  Detection and diagnosis of sensor and actuator failures using interacting multiple-model estimator , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[2]  Marcello R. Napolitano,et al.  Aircraft failure detection and identification using neural networks , 1993 .

[3]  H. Talebi,et al.  A Recurrent Neural-Network-Based Sensor and Actuator Fault Detection and Isolation for Nonlinear Systems With Application to the Satellite's Attitude Control Subsystem , 2009, IEEE Transactions on Neural Networks.

[4]  Hsien-Tsai Wu,et al.  Source number estimators using transformed Gerschgorin radii , 1995, IEEE Trans. Signal Process..

[5]  T. W. Anderson An Introduction to Multivariate Statistical Analysis , 1959 .

[6]  R. E. Kalman,et al.  A New Approach to Linear Filtering and Prediction Problems , 2002 .

[7]  J. H. Lee,et al.  Fault diagnosis and fault tolerant control of linear stochastic systems with unknown inputs. , 2001 .

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

[9]  Bor-Sen Chen,et al.  Stability Analysis of Digital Kalman Filters , 1995 .

[10]  Michael E. Polites,et al.  Autonomous Component Health Management with Failed Component Detection, Identification, and Avoidance , 2005 .

[11]  J. F. van Diejen,et al.  Calogero-Moser- Sutherland Models , 2000 .

[12]  Ranjan K. Mallik,et al.  The pseudo-Wishart distribution and its application to MIMO systems , 2003, IEEE Trans. Inf. Theory.

[13]  Youdan Kim,et al.  Experimental evaluation of fault diagnosis in a skew-configured UAV sensor system , 2011 .

[14]  Mario Innocenti,et al.  Online learning neural architectures and cross-correlation analysis for actuator failure detection and identification , 1996 .

[15]  Xudong Wang,et al.  Fault detection, identification and estimation in the electro-hydraulic actuator system using EKF-based multiple-model estimation , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[16]  Richard M. Everson,et al.  Inferring the eigenvalues of covariance matrices from limited, noisy data , 2000, IEEE Trans. Signal Process..

[17]  Pascal Larzabal,et al.  Some properties of ordered eigenvalues of a Wishart matrix: application in detection test and model order selection , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[18]  G. Bienvenu,et al.  Optimality of high resolution array processing using the eigensystem approach , 1983 .

[19]  V. Yu. Rutkovskii Fault Diagnosis and Reconfiguration in Flight Control Systems. Ch. Hajiyev and F. Caliskan. Boston: Kluwer Academic, 2003 , 2004 .

[20]  Chingiz Hajiyev,et al.  Actuator failure detection and reconfigurable control for F-16 aircraft model , 2003 .

[21]  Hong Wang,et al.  Actuator and sensor fault diagnosis of non-linear dynamic systems via genetic algorithms, neural networks, and adaptive estimation technique , 1998 .

[22]  A. N. Zhirabok Nonlinear parity relations: A logic-dynamic approach , 2008 .

[23]  Stéphane Ploix,et al.  Parity relations for linear uncertain dynamic systems , 2006, Autom..

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

[25]  Thomas Kailath,et al.  Detection of signals by information theoretic criteria , 1985, IEEE Trans. Acoust. Speech Signal Process..

[26]  Robert Grover Brown,et al.  Introduction to random signals and applied Kalman filtering : with MATLAB exercises and solutions , 1996 .

[27]  C. Tracy,et al.  The Distribution of the Largest Eigenvalue in the Gaussian Ensembles: β = 1, 2, 4 , 1997, solv-int/9707001.

[28]  Ch Hajiyev Sensor fault detection by testing the largest eigenvalue of the innovation covariance using Tracy-Widom distribution , 2010, Proceedings of the 2010 American Control Conference.

[29]  Tom E. Bishop,et al.  Blind Image Restoration Using a Block-Stationary Signal Model , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[30]  I. Johnstone On the distribution of the largest eigenvalue in principal components analysis , 2001 .

[31]  Maurice G. Kendall,et al.  The advanced theory of statistics , 1945 .

[32]  Halil Ersin Soken,et al.  Robust Estimation of UAV Dynamics in the Presence of Measurement Faults , 2012 .

[33]  Halil Ersin Soken,et al.  Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults. , 2010, ISA transactions.

[34]  Mario G. Perhinschi,et al.  Online parameter estimation issues for the NASA IFCS F-15 fault tolerant systems , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[35]  Angelo Alessandri Fault diagnosis for nonlinear systems using a bank of neural estimators , 2003, Comput. Ind..

[36]  Max Donath,et al.  American Control Conference , 1993 .

[37]  Chingiz Hajiyev,et al.  Sensor and control surface/actuator failure detection and isolation applied to F‐16 flight dynamic , 2005 .

[38]  R. Walker,et al.  Sensor Fault Detection for UAVs using a Nonlinear Dynamic Model and the IMM-UKF Algorithm , 2007, 2007 Information, Decision and Control.

[39]  Alan Edelman,et al.  The distribution and moments of the smallest eigenvalue of a random matrix of wishart type , 1991 .

[40]  Chingiz Hajiyev,et al.  Fault diagnosis and reconfiguration in flight control systems , 2003 .

[41]  Ian Postlethwaite,et al.  Sensor fault detection and accommodation using neural networks with application to a non-linear unmanned air vehicle model , 2010 .

[42]  M Steinberg,et al.  Historical Overview of Research in Reconfigurable Flight Control , 2005 .

[43]  E. C. Larson,et al.  Model-based sensor and actuator fault detection and isolation , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[44]  Raman K. Mehra,et al.  Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[45]  R. K. Mehra,et al.  Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems , 1971 .

[46]  V. F. Filaretov,et al.  Observer-based fault diagnosis in manipulation robots , 1999 .

[47]  Moe Z. Win,et al.  A General Framework for the Distribution of the Eigenvalues of Wishart Matrices , 2008, 2008 IEEE International Conference on Communications.

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

[49]  Peter S. Maybeck Multiple model adaptive algorithms for detecting and compensating sensor and actuator/surface failures in aircraft flight control systems , 1999 .

[50]  Guillaume Ducard,et al.  Efficient Nonlinear Actuator Fault Detection and Isolation System for Unmanned Aerial Vehicles , 2008 .

[51]  A. H. Mohamed,et al.  Adaptive Kalman Filtering for INS/GPS , 1999 .