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Motivation

Future air and space travel require health management functionality to ensure safe, reliable, and affordable operations as the nation implements the Next Generation Air Transportation System (NextGen) and the Vision for Space Exploration. A number of factors are driving the development of advanced health management capabilities: increasing system complexity, greater reliance on automation of vehicle functions, the need to enable effective mitigation actions through the timely detection and isolation of mission and life critical failures, to name a few.

A wide range of diagnostic algorithms have been developed for aerospace systems to enable autonomous health management and to improve system reliability and affordability. Unfortunately, a lack of support for comparative analysis of different diagnostic algorithms creates barriers to effective development and deployment of such algorithms for aerospace vehicles. This makes it difficult to understand the pros and cons of different alternatives, which might lead to sub-optimal design choices being made, with obvious unfortunate consequences for system performance and safety. We describe initial research in benchmarking diagnostic algorithms, including test datasets from a electrical power distribution system and a software framework, with the overall goals of aiding algorithm developers in maturing their techniques and informing the health management community about the suitability of various diagnostic approaches.

adapt image aircraft


Next generation spacecraft (left, exploration vehicles) and aircraft (right, Blended Wing Body)

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