New Prognostic Method Based on Spectral Analysis Techniques Dealing with Motor Static Eccentricity for Aerospace Electromechanical Actuators

The prognostic algorithms are important to identify the precursors of incipient failures of electromechanical actuators (EMA) applied to aircraft primary flight controls. Keeping in mind the risk related to the performed functions of these actuators the anticipation of an incoming failure is really useful: 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, increasing the aircraft safety and reliability. In this paper the authors propose an innovative prognostic model-based approach, able to recognize the symptoms of an EMA degradation before the explicit 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 an EMA with a 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 defined. This numerical test case simulates the dynamic behaviors of the EMA taking into account nonlinear effects related to different kinds of progressive mechanical 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 a malfunctioning, minimizing the risk of fake alarms or unannounced failures.