Turbine Engine Performance Estimation Using Particle Filters

Development of a nonlinear particle filter for engine performance is proposed. The approach employs NASA high-fidelity C-MAPSS40K engine model as the central element, and addresses the issue of lack of observability of some of the engine health parameters in previous Kalman filter formulations. Proposed approach does not require linearity of the dynamics or Gaussian noise assumptions for satisfactory operation. The feasibility of real-time implementation of the proposed approach will be demonstrated using commercial, off-the-shelf General Purpose Graphical Processing Units. Phase I feasibility demonstration will show that the particle filter formulation of the engine performance monitoring system can overcome the limitations of previously employed approaches. Phase II research will develop a prototype implementation for hardware-in-loop simulations and eventual flight test.

The NASA programs that the proposed research has a direct relevance to include Model-Based Engine Control under the Fundamental Aeronautics Program, Gas Path Health Management and Robust Propulsion Control under the Aviation Safety Program, and Fault Management Technologies under the Space Launch Systems program.

The real-time, nonlinear integrated engine performance monitoring system developed under the proposed research can be used for diagnostics and maintenance of commercial and military aircraft engines, and other gas-turbine engines. It can also be used for detecting engine performance variations and subsystem failures in flight, leading to improved commercial and military aviation safety.

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