Eddy current sensor signal processing for stall detection

This paper presents algorithms that use data from eddy current sensors mounted in the engine casing for the purpose of gas turbine engine stability monitoring. The focus of this paper is stall detection. Development of a system to detect and compensate for potentially catastrophic engine failures and instability is the primary objective of this work. A motivating objective of our engine work in general has been the development of mathematically well-founded and efficient signal processing algorithms to perform gas turbine engine blade diagnostics and high cycle fatigue prognosis using a minimal number of blade tip monitoring sensors. Our work to date has focused on the general dynamics eddy current sensor (ECS). Our ultimate goal is to extend the functionality of the ECS system beyond diagnostics to active and automatic real-time control of gas turbine engines. Blade tip sensors such as eddy current, capacitive and optical have been being used for some time now in test stand applications to detect engine faults. It has been demonstrated that they are capable of measuring tip clearance, foreign object damage (FOD), blade vibration, and stall/surge. Much of the data analysis for these methods has been performed off line; however, rapid progress is being made toward the goal of real-time detection for use in onboard flight systems. To date, most signal processing techniques using blade tip sensors have been limited to simple parametric measurements associated with the sensor waveform, e.g., measurement of zero crossing locations for time of arrival information or maxima for tip clearance information. Using this type of parametric information, many computations require more than one sensor per stage. The use of a minimal number of sensors is an extremely important practical consideration since each pound that is added to an aircraft engine adds considerable costs over the life cycle of the engine. Because of this we have focused on developing algorithms that allow the r- - eduction in the number of sensors needed for fault prognosis. We have used new parametric algorithms as well as those that make use of the entire ECS waveform. Using our algorithms we have been able to demonstrate the detection of stall cell precursors using a single ECS. These algorithms have been demonstrated in real-time in tests at the NASA Glenn W8 single stage axial-flow compressor facility. The rotor tested, designated NASA Rotor 67, is a fan with 22 blades