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- W276427409 abstract "The proposal of prognostic algorithms able to identifying the precursors of incipient failures of primary flight command electromechanical actuators (EMA) is beneficial for the anticipation of the incoming failure: a correct interpretation of the failure degradation pattern, in fact, can trig an early alert of the maintenance crew, who can properly schedule the servomechanism replacement. In this paper the authors propose an innovative prognostic model-based approach, able to recognize symptoms of an EMA degradation before the actual exhibition of the anomalous behavior. The identification/evaluation of the considered incipient failures is performed analyzing proper critical system operational parameters, able to put in evidence the corresponding degradation path, by means of a numerical algorithm based on spectral analysis techniques. Subsequently, these operational parameters are correlated with the actual health condition of the considered system by means of failure maps created by a reference monitoring model-based algorithm. In the present work, the proposed method has been applied to the case of an actuator having brushless DC motor affected by a progressive increase of the static eccentricity of the rotor. In order to evaluate the performances of the aforesaid prognostic method, a test simulation environment, able to manage different failure modes, has been conceived. This numerical test case simulates the dynamic behaviors of the EMA taking into account nonlinear effects related to different kinds of progressive failures (such as transmission backlash, friction and rotor static eccentricity). Results show that the method exhibit adequate robustness and a high degree of confidence in the ability to early identify an eventual malfunctioning, minimizing the risk of fake alarms or unannounced failures" @default.
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- W276427409 date "2015-01-01" @default.
- W276427409 modified "2023-09-27" @default.
- W276427409 title "Onboard Electromechanical Actuators Affected by Motor Static Eccentricity: a New Prognostic Method based on Spectral Analysis Techniques" @default.
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