Model Based Analysis of Precursors of Electromechanical Servomechanism Failures

Abstract

Several approaches can be employed in prognostics, to detect incipient failures of primary flight command electromechanical actuators (EMA), caused by progressive wear. The development of a prognostic algorithm capable of identifying the precursors of an electromechanical actuator failure 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. Prognostic, though, is strictly technology-oriented as it is based on accurate analysis of the cause and effect relationships. As a consequence, it is possible that prognostics algorithms that demonstrate great efficacy for certain applications (electrohydraulic actuators, for examples) fail in other circumstances, just because the actuator is based on a different technology. The research presented in this paper proposes a prognostic technique able to identify symptoms of an EMA degradation before the actual exhibition of the anomalous behavior; to this purpose friction, backlash, coil short circuit and rotor static eccentricity failures are considered. An innovative model-based fault detection neural technique is proposed to analyze information gathered through FFT analysis of the components under normal stress conditions. A proper simulation test bench was developed: 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 false alarms or unannunciated failures. ANN= Artificial Neural Network ΔV ij = ij-th phases differential voltage ̇M = effective rotor speed e ij = ij-th phases back-EMF EMA = electromechanical actuator FFT = Fast Fourier Transform ke i = i-th back-EMF constants K tr = EMA transmission shaft stiffness I i = actual i-th phase currents I ref = reference current I ref_i = reference i-th phase current V i = effective i-th phase voltages V ij = ij-th phases line-to-line voltage θ rM , θ rU = EMA motor or user position TM = BLDC motor torque TR = external aerodynamic torques T reaz = EMA transmission shaft reaction torque

Prognostics of Onboard Electromechanical Actuators: a New Approach Based on Spectral Analysis Techniques

“…deterministic methods based upon appropriate merit coefficients [7]- [8], genetic algorithms [9]- [10] or further probabilistic approaches such as the simulated annealing method [11]);  approaches based on the spectral analysis of welldefined signals (i.e. related to peculiar behaviors of the system that allow a timely identification of these incipient failures) and, generally, performed by Fast Fourier Transforms (FFT) methods [12]- [13];  hybrid approaches that exploit a suitable combination of the above methods in order to identify the health of the system [14];  identification and classification algorithms based on artificial neural networks [15]- [17]. The concepts reported in this paper are related to the design of a reliable and fast FDI routine focused on the diagnosis model-based approach and, in particular, on the parametric estimation task.…”

Electromechanical servomechanisms affected by motor static eccentricity: Proposal of fault evaluation algorithm based on spectral analysis techniques

“…To justify the fervent scientific activity in this field and the great interest shown by the aeronautical world, it must be noticed that, compared to the electrohydraulic actuations, the electromechanical actuators offer many advantages: in particular, overall weight is reduced, maintenance is simplified and hydraulic fluids, which is often contaminant, flammable or polluting, can be elimination. For these reasons, as reported by Battipede et al (2015), the use of actuation systems based on EMAs is increasing in various fields of aerospace technology. As shown in Figure 1, a typical EMA used in a primary flight control is composed by:  an actuator control electronics (ACE) that closes the feedback loop, by comparing the commanded position (FBW) with the actual one, elaborates the corrective actions and generates the reference current (I ref );  a Power Drive Electronics (PDE) that regulates the three-phase electrical power;  an electrical motor, often BLDC type;  a gear reducer having the function to decrease the motor angular speed and increase its torque;  a system that transforms rotary motion into linear motion: ball screws or roller screws are usually preferred to acme screws because have higher efficiencies and lower frictions;  a network of sensors that closes the current, angular speed and position feedback rings (RVDT).…”

A Performance Prediction Method Utilizing Time-Dependent Subsystem Transfers between Family Systems

“…Zhang et al utilized autoencoders to monitor signal features and construct a deep neural network model for the time-series prediction of equipment health indicators [20]. Vedova et al employed a combination neural network approach to identify the wear state of aircraft electro-hydrostatic actuator nozzle flapper valves [21,22] in order to achieve health status prediction [23], which represents an innovative application based on model-based fault detection and identification (FDI) methods [24], by utilizing artificial neural networks for identifying the actual wear states of actuators [25,26].…”