Neural Network based Sensor Fault Detection for Flight Control Systems

fault in aircraft is detected based on two different approaches. The first approach, well documented in literature, is based on algorithmic method dealing with Luenberger observers. The second approach, which is followed in this paper, is based on Knowledge based neural network fault detection (KBNNFD). KBNNFD uses gradient descent back propagation training algorithm of neural network. A C-Star controller of F8 aircraft model, which improves the handling qualities, is used for validation of the KBNNFD. Neural network is trained with certain features of F8 aircraft model and C-Star controller enabling it to detect the faulty sensor.A comparative analysis of both the methods is done for various cases of stuck fault of the sensor for flight control system. Nz (Normal acceleration) sensor failure was considered because of its importance in C-Star controller. Knowledge-based approach of neural network, used in this work, has come out with results indicating that it takes less time to detect the faulty Nz sensor during transition, steady state and also in the presence of random noise. Results show improvement when compared to algorithmic methods with regard to the time taken to detect faults and ability to detect sensor faults especially near steady state. Investigations have been carried out using Matlab and Simulink.

[1]  M. N. Wagdi.  An adaptive control approach to sensor failure detection and isolation , 1982 .

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

[3]  J. A. Hauge,et al.  F-8C digital CCV flight control laws , 1976 .

[4]  Leonard A. Smith,et al.  Indistinguishable states II. The imperfect model scenario , 2004 .

[5]  R. Clark,et al.  Sensor Fault Detection in a System with Random Disturbances , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[6]  Guillermo Heredia,et al.  Sensor and actuator fault detection in small autonomous helicopters , 2008 .

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

[8]  Thomas Kerr,et al.  Decentralized Filtering and Redundancy Management/Failure Detection for Multisensor Integrated Navigation Systems , 1985 .

[9]  H. A. Mooij.  Criteria for Low-Speed Longitudinal Handling Qualities , 1985 .

[10]  Jacek M. Zurada,et al.  Introduction to artificial neural systems , 1992 .

[11]  Reconstructed Flight Control Sensor Signals Via Luenberger Observers , 1979, IEEE Transactions on Aerospace and Electronic Systems.

[12]  Raymond C. Montgomery,et al.  Failure Accommodation in Digital Flight Control Systems by Bayesian Decision Theory , 1976 .

[13]  Brad Seanor,et al.  A fault tolerant flight control system for sensor and actuator failures using neural networks , 2000 .

[14]  Rama,et al.  An Aid for Mechanization of Flight Control Systems on Microcomputers , 2001 .

[15]  Mario Innocenti,et al.  Kalman filters and neural-network schemes for sensor validation in flight control systems , 1998, IEEE Trans. Control. Syst. Technol..

[16]  Kumpati S. Narendra,et al.  Control of nonlinear dynamical systems using neural networks: controllability and stabilization , 1993, IEEE Trans. Neural Networks.

[17]  S. A. Dunn,et al.  Fault Detection, Identification and Accommodation Techniques for Unmanned Airborne Vehicles , 2005 .