An enhanced model based approach to monitor friction within Electromechanical Actuator (EMA) ballscrews using motor current is presented. The research was motivated by a drive in the aerospace sector to implement EMAs for safety critical applications to achieve a More Electric Aircraft (MEA). Concerns in reliability and mitigating the single of point of failure (ballscrew jamming) have resulted in consideration of Prognostics and Health Monitoring (PHM) techniques to identify the onset of jamming using motor current. A higher fidelity model based approach is generated for a true representation of ballscrew degradation, whereby the motor is modelled using ‘dq axis’ transformation theory to include a better representation of the motor dynamics. The ballscrew kinematics are to include the contact mechanics of the main sources of friction through the Stribeck model. The simulations demonstrated feature extraction of the dynamic behaviour in the system using Iq current signals. These included peak starting current features during acceleration and transient friction variation. The simulated data were processed to analyse peak Iq currents and classified to represent three health states (Healthy, Degrading and Faulty) using k-Nearest Neighbour (k-NN) algorithm. A classification accuracy of ~74% was achieved.
Prognostics, Health Monitoring, Aerospace, Electromechanical Actuators, Ballscrew, Fault Classification
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