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Electromechanical actuators have been gaining increased acceptance as safety-critical actuation devices in the next generation of aircraft and spacecraft. The aerospace manufacturers are not ready, however, to completely embrace electromechanical actuators for all applications due to apprehension with regard to some of the more critical fault modes. This work aims to help address these concerns by developing and testing a prognostic health-management system that diagnoses electromechanical actuator faults and employs prognostic algorithms to track fault progression and predict the actuator's remaining useful life. The diagnostic algorithm is implemented using a combined modelbased and data-driven reasoner. The prognostic algorithm, implemented using Gaussian process regression, estimates the remaining life of the faulted component. The paper also covers the selection of fault modes for coverage and methods developed for fault injection. Validation experiments were conducted in both laboratory and flight conditions using a flyable electromechanical actuator test stand. The stand allows test actuators to be subjected to realistic environmental and operating conditions while providing the capability to safely inject and monitor propagation of various fault modes. The paper covers both diagnostic and prognostic run-to-failure experiments, conducted in laboratory and flight conditions for several types of faults. The experiments demonstrated robust fault diagnosis on the selected set of component and sensor faults and high-accuracy predictions of failure time in prognostic scenarios.

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Electro-Mechanical Actuators (EMA) are gaining prominent roles in the next generation fly-by-wire aircraft and spacecraft. With these roles often being safety-critical (control surface or landing gear actuation, for instance), the key to faster adoption of EMA in aerospace applications is development of accurate and reliable prognostic health management (PHM) systems that not only detect and identify faults, but also predict how the identified they affect the remaining useful life (RUL) of both the faulty component and the actuator as a whole. Such information can be invaluable to pilots, controllers, and maintenance personnel in assessing how to complete or re-plan the desired mission with a sufficient safety margin. A team consisting of members of NASA Ames Diagnostic & Prognostic Group has developed a prototype PHM system for EMA that provides coverage for a number of faults modes typical to this type of actuators. The diagnostic portion of the system is implemented using a hybrid approach which utilizes both qualitative (bond graph, model-based) and quantitative (data-driven) reasoners to achieve low false positive and false negative detection rates and a high accuracy of diagnostic output. Once a fault has been diagnosed, the prognostic component, which is implemented using Gaussian Process Regression (GPR) principles, estimates the RUL of the component that is faulted. Experiments were conducted both in laboratory and flight conditions to validate the PHM system using an innovative Flyable Electromechanical Actuator (FLEA) test stand. The test stand allows experimental actuators to be subjected to environmental and operating conditions similar to what actuators on the host aircraft are experiencing, while providing researchers with the capability to safely inject and monitor propagation of various fault modes. Prognostic run-to-failure experiments were done in laboratory conditions on ballscrew jam and motor winding short faults. Flight experiments (albeit not run-to-failure) were conducted in collaboration with the US Army on UH-60 Blackhawk helicopters. The paper describes these experiments in detail and presents the results obtained from the PHM system with regard to the estimation of the RUL of the actuator.

Expanded deployment of Electro-Mechanical Actuators (EMAs) in critical applications has created much interest in EMA Prognostic Health Management (PHM), a key enabling technology of Condition Based Maintenance (CBM). As such, Impact Technologies, LLC is collaborating with the NASA Ames Research Center to perform a number of research efforts in support of NASA's Integrated Vehicle Health Management (IVHM) initiatives. These efforts have combined experimental test stand development, laboratory seeded fault testing, and physical model-based health monitoring in a comprehensive PHM system development strategy. This paper discusses two closely related EMA research programs being conducted by Impact and NASA Ames. The first of these efforts resulted in the creation of an electro-mechanical actuator test stand for the Prognostics Center of Excellence at the NASA Ames Research Center. The second effort is ongoing and is utilizing physics-based modeling techniques to develop an algorithm and software package toolset for PHM of aircraft EMA systems using a hybrid (virtual sensor) approach. 1,2