Gearbox Degradation Prediction through Deep CNN and Bayesian Optimization
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Abstract
The original dataset, recorded at a high sampling rate of 20,480 Hz, presents a considerable volume of data. To facilitate efficient processing and analysis, we implement a data reduction strategy, which involves downsampling the signal to a more manageable frequency of 2,048 Hz.
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Grearbox, Degradation, Deep CNN, Bayesian Optimization
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