Aircraft Electrical Power System Diagnostics and Health Management

Prognostics and health management is a critical technology for accurately predicting impending failures and providing a decision process for replacing components before actual failures occur. This is particularly critical in aircraft Electrical Power Systems. Significant gains in maintenance decision making, safety, system availability, productivity, and cost savings, could be realized by the Navy with a capability that predicts impending failures in these systems. This effort investigates the feasibility of exploiting advances in monitoring and prognostics technologies to develop a comprehensive health and usage monitoring system capability for aircraft electrical power systems. Considerations include the application of a real-time, sensory- updated, residual life based prognostics methodology to provide the current and predicted degradation states of critical components such as generators, converters and batteries. The effort researches and characterizes the faults and physical phenomena of the degradation process, researches condition monitoring technology options, characterizes the patterns in the sensory information to develop an appropriate degradation model , and identifies the modeling approach required to model the evolution of the component degradation and predict its residual life based on identified failure thresholds. A system definition, capability implementation plan, and a basic proof-of-concept validation of the technical approach are part of this effort. BENEFITS: The prognostics technology developed under this effort can be easily applied in other domains such as sea, space and ground vehicle platforms, to advance the implementation of Condition Based Maintenance (CBM) principles. In addition, there is a big potential for commercialization. For example, the prognostics technology resulting from this effort can be applied in other industries including, commercial aviation, power utilities, automotive and any commercial plants where failures in large scale manufacturing systems have a great economic impact.