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- W2183394732 abstract "Thispaperconcentrates ona newprocedure whichexperimentally recognizes gears andbearings faults ofa typical gearbox systemusing amulti-layer perceptron neural network. Feature vector whichisoneofthemostsignificant parameters todesign an appropriate neuralnetworkwas innovated bystandard deviation ofwavelet packet coefficients. Thegearconditions wereconsidered tobenormal gearbox and slight- andmedium-worn andbroken-teeth gearsfaults anda general bearing fault whichwerefive neuronsofoutput layer withtheaimoffault detection andidentification. A downscaled 2-layer Multi-Layer Perceptron neural-network-based system withgreataccuracy wasdesigned tocarryoutthetask. Vibration signals wererecognized asthemostreliable source to extract thefeature vectorwhichwerebypiecewise cubic hermite interpolation synchronized andpre-processed using the standard deviation ofwavelet packetcoefficients inthis research." @default.
- W2183394732 created "2016-06-24" @default.
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- W2183394732 date "2005-01-01" @default.
- W2183394732 modified "2023-10-12" @default.
- W2183394732 title "A Fault Detection andIdentification System for GearboxesusingNeuralNetworks" @default.
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