Neural network-based helicopter gearbox health monitoring system

The results of two neural hardware implementations of a helicopter gearbox health monitoring system (HMS) are summarized. The first hybrid approach and implementation to fault diagnosis is outlined, and results are summarized using three levels of fault characterization: fault detection (fault or no fault), classification (hear or bearing fault), and identification (fault sub-classes). Initial hardware results compare well with previously published software simulations. The second all-analog implementation exploits the ability of analog neural hardware to compute the discrete Fourier transform (DFT) as a preprocessor to a neural classifier.<
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