An NVIDIA Jetson graphical processing unit was evaluated for utilization in a health and usage monitoring system by computing vibration-based condition indicators, evaluating autoencoders for anomaly detectors, and comparing the computational performances to the related performance when the computations were performed on the central processing unit. The comparison included signal preprocessing computations. Two distinct cases of interest were considered with neural network autoencoders: model evaluation and model adaptation with limited training. The experiments found that computations associated with signal preprocessing and computing of condition indicators performed faster on the central processing unit, but neural network model evaluation and adaptation were faster on the graphical processing units. Utilizing the GPU capability of the Jetson Nano, it was determined that 42 accelerometer signals could be evaluated through an autoencoder per second.
How to Cite
GPU, CPU, Vibration, Autoencoders, Deep learning, HUMS
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