Fault diagnosis in a flight actuator using extended Kalman filter parameter estimator

Jayakumar M., Das Bijan B.

Abstract:
Expeditious diagnosis and prognosis of faults in mission critical systems is essential to initiate proactive steps like mission salvage to prevent loss of vehicle in presence of impending failures and allow condition based maintenance scheduling. Use of analytical redundancy based fault diagnosis techniques permits deep diagnosis of system faults and offers advantages in cost, weight, power consumption and reliability over conventional fault diagnosis schemes. This paper presents the application of Extended Kalman Filter (EKF) parameter estimator for diagnosis of process faults in an electromechanical flight control actuation system. The detailed model of the actuator including mounting structure stiffness, load dynamics, compensators, power amplifier and friction non-linearity is considered. The EKF is configured as a parameter estimator for estimating the motor parameters like resistance, inductance, torque constant, etc. Deviations of the estimated parameters from nominal values are used to generate analytic symptoms indicative of impending process faults in the system. A-priori knowledge of basic relationships between faults and symptoms combined with fuzzy inference is used for fault diagnosis. Simulation results indicate that the diagnosis scheme formulated is capable of efficiently identifying process faults like winding short, commutator failures, etc., in flight actuators.

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