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This document discusses using fast Fourier transformation and artificial neural networks to analyze DC arcing faults in spacecraft power systems. It describes an experiment conducted by NASA Glenn Research Center to collect voltage and current data from DC arcing faults. Fourier analysis is used to extract frequency domain features from the signals, which are then used to train a neural network to identify and classify different fault types.

Author

363331272

Language

EN