Experimental Validation of a Prognostic Health Management System for Electro-Mechanical Actuators

The work described herein is aimed to advance prognostic health management solutions for electro-mechanical actuators and, thus, increase their reliability and attractiveness to designers of the next generation aircraft and spacecraft. In pursuit of this goal the team adopted a systematic approach by starting with EMA FMECA reviews, consultations with EMA manufacturers, and extensive literature reviews of previous efforts. Based on the acquired knowledge, nominal/off-nominal physics models and prognostic health management algorithms were developed. In order to aid with development of the algorithms and validate them on realistic data, a testbed capable of supporting experiments in both laboratory and flight environment was developed. Test actuators with architectures similar to potential flight-certified units were obtained for the purposes of testing and realistic fault injection methods were designed. Several hundred fault scenarios were created, using permutations of position and load profiles, as well as fault severity levels. The diagnostic system was tested extensively on these scenarios, with the test results demonstrating high accuracy and low numbers of false positive and false negative diagnoses. The prognostic system was utilized to track fault progression in some of the fault scenarios, predicting the remaining useful life of the actuator. A series of run-to-failure experiments were conducted to validate its performance, with the resulting error in predicting time to failure generally lesser than 10% error. While a more robust validation procedure would require dozens more experiments executed under the same conditions (and, consequently, more test articles destroyed), the current results already demonstrate the potential for predicting fault progression in this type of devices. More prognostic experiments are planned for the next phase of this work, including investigation and comparison of other prognostic algorithms (such as various types of Particle Filter and GPR), addition of new fault types, and execution of prognostic experiments in flight environment.