Development of Regime Recognition Tools for Usage Monitoring

Usage monitoring entails determining the actual usage of a component on the aircraft and requires accurate recognition of regimes. In this paper, a data mining approach is adopted for regime recognition. In particular, a regime recognition algorithm developed based on hidden Markov models is presented. The developed algorithm was validated using the flight card data of an Army UH-60L helicopter. The performance of this regime recognition algorithm was also compared with other data mining methods using the same dataset. Using the flight card information and regime definitions, a dataset of about 56,000 data points labeled with 50 regimes recorded in the flight card were mapped to the health and usage monitoring parameters. The validation and performance comparison results have showed that the hidden Markov model based regime recognition algorithm was able to accurately recognize the regimes recorded in the flight card data and outperformed other data mining methods.