UAV Analytical Redundancy based fault detection of the Airspeed Sensor via Generalized Likelihood Ratio Test

The purpose of this paper is to evaluate the performance of a sequential Generalized Likelihood Ratio Test (GLRT) for the detection of possible faults occurring on the airspeed velocity sensor of an UAV based on real flight data from the WVU YF-22 research aircraft. In the first part of the paper a novel Analytical Redundancy based method is proposed to provide a real-time estimation of the airspeed velocity to be use later for fault detection purpose. In this approach, assuming the availability of the state and input signals governing the UAV dynamics, the angle of attack equation is rearranged as a quadratic equation in V(k) which is solved to provide estimates of the airspeed. The parameters governing the airspeed equation are identified via LS optimization from a record of flight data. The error signal between the measured airspeed velocity V(k) and its estimation provides the residual signal r(k) that is used for fault detection. In order to apply a sequential GLRT it was necessary to design a residual whitening filter so that rigorous statistical tests can be performed on the Gaussian whitened signal. Under this assumption it is possible to compute the probability of false alarms. In the experimental part, given a probability of false alarm the detectability of hard and soft failures was studied by varying the amplitude and rate of faults artificially injected on the experimental flight data. The experimental results confirm the capabilities of the proposed method to cope with the important problem of the airspeed sensor fault detection.

[1]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[2]  Christine M. Belcastro,et al.  Aircraft Loss-of-Control Accident Analysis , 2010 .

[3]  James A. Brass,et al.  Imaging from an unmanned aerial vehicle: agricultural surveillance and decision support , 2004 .

[4]  Tarek Hamel,et al.  A UAV for bridge inspection: Visual servoing control law with orientation limits , 2007 .

[5]  D. N. Shields,et al.  Design of nonlinear observers for detecting faults in hydraulic sub-sea pipelines , 2001 .

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

[7]  P. Valigi,et al.  Model-based approaches for the airspeed estimation and fault monitoring of an Unmanned Aerial Vehicle , 2012, 2012 IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS).

[8]  Mario G. Perhinschi,et al.  Modeling and Simulation of Failures for Primary Control Surfaces , 2002 .

[9]  R. Patton,et al.  A Review of Parity Space Approaches to Fault Diagnosis , 1991 .

[10]  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.

[11]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

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

[13]  Giampiero Campa,et al.  Design of robust redundancy relations for a semi-scale YF-22 aircraft model , 2009 .

[14]  Matthew Johnson-Roberson,et al.  Airborne smoothing and mapping using vision and inertial sensors , 2009, 2009 IEEE International Conference on Robotics and Automation.

[15]  Mario G. Perhinschi,et al.  Machine Vision/GPS Integration Using EKF for the UAV Aerial Refueling Problem , 2008, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[16]  Rolf Isermann,et al.  Supervision, fault-detection and fault-diagnosis methods — An introduction , 1997 .

[17]  David W. Jacobs,et al.  Whitening for Photometric Comparison of Smooth Surfaces under Varying Illumination , 2004, ECCV.

[18]  Mario G. Perhinschi,et al.  Integration of Fault Tolerant System for Sensor and Actuator Failures Within the WVU NASA F-15 Simulator , 2003 .

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

[20]  B.D. Appleby,et al.  Using learning techniques to accommodate unanticipated faults , 1993, IEEE Control Systems.

[21]  Ryosuke Shibasaki,et al.  UAV-Borne 3-D Mapping System by Multisensor Integration , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Mario G. Perhinschi,et al.  Modelling and Simulation of a Fault-Tolerant Flight Control System , 2006 .

[23]  Sirish L. Shah,et al.  Structured residual vector-based approach to sensor fault detection and isolation , 2002 .

[24]  Christine M. Belcastro,et al.  Future Integrated Systems Concept for Preventing Aircraft Loss-of-Control Accidents , 2010 .

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

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

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

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

[29]  Spilios D. Fassois,et al.  A Statistical Method for the Detection of Sensor Abrupt Faults in Aircraft Control Systems , 2008, IEEE Transactions on Control Systems Technology.

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