Title :
Particle filter based anomaly detection for aircraft actuator systems
Author :
Brown, D. ; Georgoulas, G. ; Bae, H. ; Vachtsevanos, G. ; Chen, R. ; Ho, Y.H. ; Tannenbaum, G. ; Schroeder, J.B.
Author_Institution :
Georgia Inst. of Technol., Atlanta, GA
Abstract :
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.
Keywords :
Bayes methods; Hilbert transforms; aerospace computing; aircraft control; aircraft instrumentation; aircraft power systems; belief networks; brushless DC motors; computerised instrumentation; electric actuators; electromechanical effects; failure analysis; fault location; feature extraction; machine insulation; machine windings; Bayesian estimation theory; FMEA; Hilbert transforms; LabVIEW; Simulink model; aircraft electromechanical actuator system; brushless DC motor winding insulation faults; failure modes-and-effects analysis; feature extraction routine preprocess; particle filter based anomaly detection; physics-of-failure mechanism; turn-to-turn insulation winding faults; Actuators; Aircraft; Brushless DC motors; Context modeling; Detectors; Failure analysis; Fault detection; Fault diagnosis; Insulation; Particle filters;
Conference_Titel :
Aerospace conference, 2009 IEEE
Conference_Location :
Big Sky, MT
Print_ISBN :
978-1-4244-2621-8
Electronic_ISBN :
978-1-4244-2622-5
DOI :
10.1109/AERO.2009.4839659