Techniques are described for determining a remaining useful life (RUL) prognosis of bearings using a feature extraction module for extracting time series normalized similarity (TSNS) features for vibration data normalization and a prediction module utilizing a deep learning model, known as an independently recurrent neural network (IndRNN), for predicting bearing RUL. The feature extraction module and prediction module are deployed on a fog computing platform as services for determining the RUL prognosis of bearings.
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Jiao, Cheng and Liu, Tonny, "FOG COMPUTING BASED BEARING REMAINING USEFUL LIFE PROGNOSIS USING TIME SERIES NORMALIZED SIMILARITY AND RECURRENT NEURAL NETWORKS", Technical Disclosure Commons, (March 06, 2019)