This brief paper proposes a dynamic data-driven method for stability monitoring of rotorcraft systems, where the underlying concept is built upon the principles of symbolic dynamics. The stability monitoring algorithm involves wavelet-packet-based preprocessing to remove spurious disturbances and to improve the signal-to-noise ratio (SNR) of the sensor time series. A quantified measure, called Instability Measure, is constructed from the processed time series data to obtain an estimate of the relative instability of the dynamic modes of interest on the rotorcraft system. The efficacy of the proposed method has been established with numerical simulations where correlations between the instability measure and the damping parameter(s) of selected dynamic mode(s) of the rotor blade are established.
Issue Section:
Technical Brief
Keywords:
anomaly detection,
rotorcraft instability,
probabilistic finite state automata,
damping estimation
Topics:
Algorithms,
Damping,
Dynamics (Mechanics),
Rotors,
Sensors,
Stability,
Time series,
Wavelets,
Simulation,
Signals,
Blades,
Dynamic systems
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