Anomaly Detection and Prognosis for Primary Flight Control EMAs
One of the most significant research trends in the aeronautic industry is currently the design and, possibly, build of “more electric aircraft”. In this framework, one of the more deeply investigated subjects has been, and still is, the replacement of the traditional hydraulic/electro-hydraulic technology for flight control systems with the electromechanical ones. Although featuring many advantages, electro-mechanical actuators still suffer from several shortcomings, mainly those related to reliability issues, which are still difficult to overcome simply by design. The development of an efficient PHM system could instead provide the needed increase in reliability without any major design variations. This paper addresses, in the first part of the study, the design of a comprehensive PHM system for EMAs employed as primary flight control devices; the peculiarities of the application are presented and discussed, while a novel approach based on short pre-flight health tests is proposed. The most common electric motor windings degradation is addressed in the second part and a particlefiltering framework for anomaly detection and prognosis is proposed featuring a self-tuning non-linear model for improved prognostic performance. Features, anomaly detection and the prognostic algorithm are hence evaluated through state-of-the art performance metrics and their results discussed.
How to Cite
Anomaly Detection, Failure Prognosis, Particle Filtering
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