Experimental Validation of Multi-fidelity Models for Prognostics of Electromechanical Actuators

Abstract

The growing adoption of electrical energy as a secondary form of onboard power leads to an increase of electromechanical actuators (EMAs) use in aerospace applications. Therefore, innovative prognostic and diagnostic methodologies are becoming a fundamental tool to early identify faults propagation, prevent performance degradation, and ensure an acceptable level of safety and reliability of the system. Furthermore, prognostics entails further advantages, including a better ability to plan the maintenance of the various equipment, manage the warehouse and maintenance personnel, and a reduction in system management costs. Frequently, such approaches require the development of typologies of numerical models capable of simulating the performance of the EMA with different levels of fidelity: monitoring models, suitably simplified to combine speed and accuracy with reduced computational costs, and high fidelity models (and high computational intensity), to generate databases, develop predictive algorithms and train machine learning surrogates. Because of this, the authors developed a high-fidelity multi-domain numerical model (HF) capable of accounting for a variety of physical phenomena and gradual failures in the EMA, as well as a low-fidelity counterpart (LF). This simplified model is derived by the HF and intended for monitoring applications. While maintaining a low computing cost, LF is fault sensitive and can simulate the system position, speed, and equivalent phase currents. These models have been validated using a dedicated EMA test bench, designed and implemented by authors. The HF model can simulate the operation of the actuator in nominal conditions as well as in the presence of incipient mechanical faults, such as a variation in friction and an increase of backlash in the reduction gearbox. Comparing the preliminary results highlights satisfactory consistency between the experimental test bench and the two numerical models proposed by the authors.

Prognostics of aerospace electromechanical actuators: comparison between model-based metaheuristic methods

“…The MM is characterized by a series of predefined Top Level Parameters (TLPs) which correspond to different failures: by changing these parameters the model will output trends with the specified failure injected. Both models have been validated in nominal conditions as well as in the presence of different failures and non linearities, confirming the models ability in estimating actual trends with high precision [19,20].…”

“…• Controller: a PID controller compares the desired position and signal with the user position and speed and outputs the reference current sent to the inverter; • Inverter: it derives the three currents and voltages (one for each phase) for the PMSM motor and performs the corresponding PWM modulation; • Sinusoidal BLDC motor: it receives as input position and angular velocity and supplies the required motor torque; • Motor-transmission dynamics: it calculates the dynamics of the motor-transmission module (i.e. second-order dynamics and multiple non-linearities [19]).…”

A Model-Based Prognostic Framework for Electromechanical Actuators based on Metaheuristic Algorithms

“…• Motor-transmission dynamics: this final block compares the available torque with the external requested torque and solves a second-order dynamical system comprehensive of multiple non linearities, such as dry friction and backlash [34]). The outputs of this block are the motor position and speed, which are looped back to the controller.…”