Aircraft Electrical Power System Diagnostics and Health Management

Based on the research and analysis conducted during Phase I, it was determined that it is feasible to detect, diagnose, predict and manage impending failures in a rotating electrical machine (e.g., aircraft generator) using a real-time, sensory-updating residual life distribution estimation technique. Furthermore, the methodology allows us to develop a prognostic capability for the electrical power system components without the great expense of running components to failure. That is, the degradation based prognostic technique is an efficient way to develop predictive models in the absence of a prior sample of degradation signals. This is one of the most significant breakthroughs of this research effort. The feasibility was demonstrated analytically and through an experimental setup (rotating machine) in order to (a) test the alternative of using only in-service failure time data to evaluate the distribution of the stochastic parameters of the degradation model and (b) test a hypothesis about the functional form that a component’s degradation signal will follow, in the absence of a database of degradation signals.