The objective of this proposal is to demonstrate the ability of Beaver Aerospace and Defense to decrease the weight and cost of aircraft actuators while increasing their reliability. Aircraft actuators are a significant cost and safety concern to the Department of Defense. Beaver Aerospace and Defense is developing a Self-Learning Algorithm for System Health (SLASH) that monitors critical aircraft actuator components and uses a prognostic set of equations to predict early wear and failure probabilities. The equations and equipment used are directly applicable to subsystem and system health in aircraft.
Benefit: The net effect of integrating smart actuators and SLASH is to enhance vehicle control and increase vehicle autonomy. There is an obvious tie in to networking and machine-to-machine integration, since the system would automatically look for ways to increase system performance, increase life and decrease wear factors, and avoid wear through the selection of optimum performance parameters. The development of these concepts has military worth through its improvement of performance, maintainability, and reliability. The anticipated benefits of SLASH are that it could be used to monitor all aircraft electro-mechanical components and prognosticate on overall aircraft system health. It is possible to use the system to determine the probability of failure of critical components and provide an early warning of maintenance and safety risks. This could be an add-on safety system or integrated into flight controls or computers.
Keywords: Self-Learning Algorithm for System Health, Self-Learning Algorithm for System Health, Replacing Hydraulics with electromechanical actuators, Weight Savings, smart actuator, relaibility
