The key to material damage state sensor systems making the transition from the laboratory to on-wing is to improve overall system robustness. It is important to develop simple, robust algorithms that can be coupled together with survivable, reliable sensors for turbine blade prognosis to be successful. One of the keys to developing these systems is the ability to quickly and inexpensively develop a large enough data set for simulating engine operating conditions and material damage state conditions. This proposal outlines an approach using a high temperature microwave tip clearance sensor along with a sub-scale, commercial off-the-shelf (COTS) based spin rig to develop a large data set for algorithm development. The resulting data set will be used to develop robust algorithms that track material damages states over normal engine operating conditions. The end result of the project is a complete sensor system able to detect and trend material state as part of an on-wing prognosis system.
