This paper describes the background, simulation and experimental evaluation of an anomaly detector for Brushless DC motor winding insulation faults in the context of an aircraft Electro-Mechanical Actuator (EMA) application. Results acquired from an internal Failure Modes and Effects Analysis (FMEA) study identified turn-to-turn winding faults as the primary mechanism, or mode, of failure. Physics-of-failure mechanisms used to develop a model for the identified fault are provided. The model was implemented in Simulink to simulate the dynamics of the motor with a turn-to-turn insulation winding fault. Then, an experimental test procedure was devised and executed to validate the model. Additionally, a diagnostic feature, identified by the fault model and derived using Hilbert transforms, was validated using the Simulink model and experimental data for several fault dimensions. Next, a feature extraction routine preprocesses monitoring parameters and passes the resulting features to a particle filter. The particle filter, based on Bayesian estimation theory, allows for representation and management of uncertainty in a computationally efficient manner. The resulting anomaly detection routine declares a fault only when a specified confidence level is reached at a given false alarm rate. Finally, the real-time performance of the anomaly detector is evaluated using LabVIEW.
Particle filter based anomaly detection for aircraft actuator systems
01.03.2009
1749133 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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