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Date of Award
Thesis - Open Access
Master of Science in Aerospace Engineering
Dr. Richard "Pat" Anderson
Dr. Nickolas "Dan" Macchiarella
Professor Charles Eastlake
The purpose of this study is the application of Kalman filters to real-time Flight Regime Recognition (FRR) algorithms to identify the regime flown and observe transitions between flight regimes. Rotor fault identification, a technique that is somewhat similar to flight regime recognition, successfully used Kalman filters to determine fault types and damage locations. Recently developed FRR algorithms successfully applied Hidden Markov Models, which are similar to Kalman filters. The selected regime set for this study derives from a study performed by Bell Helicopter Textron, Inc. The selected parameter set for this study is modified from the Schweizer 300 Flight Test Program performed by Embry-Riddle Aeronautical University. The FRR algorithms developed will use the recorded flight parameters to identify a flight regime. A graphical interface allows the user to observe the real-time FRR and transitions between regimes. This research aims to bridge the gap between the application of mathematical models for damage identification and regime recognition. Multiple mathematical models developed for rotor blade fault and damage identification include neural networks, fuzzy logic systems, and Kalman filters. Recent research indicates that only the neural network approach has been applied to FRR algorithms, and that a Hidden Markov Model (HMM) approach outperformed the neural network. Additionally, public domain regime recognition research focuses on post processing algorithms rather than real-time regime recognition. The post processing codes appear to use discrete algorithms, which do not clearly identify transitions between regimes.
Scholarly Commons Citation
Rajnicek, Rachel Elizabeth, "Application of Kalman Filtering to Real-time Flight Regime Recognition Algorithms in a Helicopter Health and Usage Monitoring System" (2008). Theses - Daytona Beach. 169.