Abstract
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.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
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)
https://www.tdcommons.org/dpubs_series/2012