Helicopter gearbox fault detection and diagnosis using analog neural networks

This paper summarizes the results of two neural hardware implementations of a helicopter gearbox health monitoring system (HMS). Our first hybrid approach and implementation to fault diagnosis is outlined, and our results are summarized using three levels of fault characterization: fault detection (...

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Hauptverfasser: Monsen, P.T., Manolakos, E.S., Dzwonczyk, M.
Format: Tagungsbericht
Sprache:eng
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Beschreibung
Zusammenfassung:This paper summarizes the results of two neural hardware implementations of a helicopter gearbox health monitoring system (HMS). Our first hybrid approach and implementation to fault diagnosis is outlined, and our results are summarized using three levels of fault characterization: fault detection (fault or no fault), classification (gear or bearing fault), and identification (fault sub-classes). Our second all-analog implementation exploits the ability, of analog neural hardware to compute the discrete Fourier transform (DFT) as a pre-processor to a neural classifier. Our hardware results compare well with previously published software simulations.< >
ISSN:1058-6393
2576-2303
DOI:10.1109/ACSSC.1993.342539