Unmanned Air Systems (UAS) show great promise for a range of civilian applications, especially ‘dull, dirty or dangerous’ missions such as air-sea rescue, coastal and border surveillance, fisheries protection and disaster relief. As the demand for autonomy increases, the importance of correctly identifying and responding to faults becomes more apparent, as fully autonomous systems must base their decisions solely upon the sensors readings they receive – as there is no human on board.

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies.

A prognostics and health management (PHM) system is the focus of attention in the Defense Acquisition University’s LOG 201 Intermediate Acquisition Logistics course. LOG 201 is a one-week case-based course required for Level II certification in the Department of Defense Life-cycle Logistics career field, as required by the Defense Acquisition Workforce Improvement Act (DAWIA). In this hypothetical case, the PHM System is to be embedded in the Strike Talon Unmanned Combat Aircraft System for use by the United States Air Force and Navy.