A modelling approach to monitor ballscrew friction within Electromechanical Actuators (EMA) using motor current is presented along with subsequent fault diagnostics using classification of simulated data for healthy, degrading and faulty states. An approach was used where a baseline linear EMA system was modelled to a high level of detail. The modelling involved emphasis on the Permanent Magnet Synchronous Motor (PMSM) where a greater understanding of the drivetrain could be achieved. The PMSM was modelled using ‘dq axis’ transformation theory. The mechanical elements of the EMA were also modelled to include non-linear characteristics. Interaction between the ball and nut, and ball and screw are considered the main source of friction within the ballscrew, hence sliding velocities in these contact areas were used to calculate velocity dependent friction using the Stribeck friction model. Contact angles between ball and nut, and ball and screw, and mechanical efficiencies were varied to analyse the effect on the torque producing current for healthy, degrading and faulty conditions. The simulated data was trained for each condition for classification using a k-Nearest Neighbour (k-NN) algorithm. The first part of the analysis revealed that ballscrew degradation should be detectable using motor current by monitoring changes to the torque producing q-axis current for each failure state in the ballscrew damage model. External load disturbances were also modelled since they could cause fluctuations to the q-axis currents thus making it
difficult to isolate deteriorations to the ballscrew. The simulated datasets were processed for classification as training data using the k-NN algorithm where a classification accuracy of ~74% was achieved. Overall, the in-depth
modelling of the EMA system presented a comprehensive approach to monitoring ballscrew friction through use of motor current analysis from different test cases. It is proposed that employing a hybrid approach (combination of model based and data driven techniques) to fault diagnostics can further improve the classification accuracy.
Prognostics, Health Monitoring, Aerospace, Electromechanical Actuators, Ballscrew, Fault Classification
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