Prognosis of Wear Progression in Electrical Brakes for Aeronautical Applications

Abstract

The evolution towards “more electric” aircrafts has seen a decisive push in the last decade, due to the growing environmental concerns and the development of new market segments (flying taxis). Such push interested both the propulsion components and the aircraft systems, with the latter seeing a progressive trend in replacing the traditional solutions based on hydraulic power with electrical or electromechanical devices. Although more attention is usually devised towards the flight control actuation, an interesting and fast-developing application field for electro-mechanical systems is that of the aeronautical brakes. Electro-mechanical brakes, or E-Brakes hereby onwards, would present several advantages over their hydraulic counterparts, mainly related to the avoidance of leakage issues and the simplification of the system architecture. The more difficult heat dissipation, associated with the thermal issues that usually constitute one of the most significant sizing constraints for electromechanical actuators, limits so far, their application (or proposal of application) to light-weight vehicles. Within this context, the development of PHM solutions would align with the need for an on-line monitoring of a relatively unproven component. This paper deals with the preliminary stages of the development of such PHM system for an E-Brake to be employed on a future executive class aircraft, where the brake is actuated through four electro-mechanical actuators. Since literature on fault diagnosis and prognosis for electrical motors is fairly extensive, we focused this preliminary analysis on the development of PHM techniques suitable to monitor and prognose the evolution of the brake pads wear instead. The paper opens detailing the system architecture and continues presenting the high-fidelity dynamic model used to build synthetic data-sets representative of the possible operating conditions faced by the E-Brake within realistic operative scenarios. Such data are then used to foster a preliminary feature selection process, where physics-based indexes are compared and evaluated. Simulated degradation histories are then used to test the application of data-driven fault detection algorithm and the possible application of particle-filtering routines for prognosis.