Related references
Note: Only part of the references are listed.Unintrusive Monitoring of Induction Motors Bearings via Deep Learning on Stator Currents
Francesca Cipollini et al.
INNS CONFERENCE ON BIG DATA AND DEEP LEARNING (2018)
A recurrent neural network based health indicator for remaining useful life prediction of bearings
Liang Guo et al.
NEUROCOMPUTING (2017)
Degradation Feature Selection for Remaining Useful Life Prediction of Rolling Element Bearings
Bin Zhang et al.
QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL (2016)
An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings
Naipeng Li et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2015)
Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis
Chuan Li et al.
NEUROCOMPUTING (2015)
Failure diagnosis using deep belief learning based health state classification
Prasanna Tamilselvan et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2013)
A data-model-fusion prognostic framework for dynamic system state forecasting
J. Liu et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2012)
Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics
H Qiu et al.
JOURNAL OF SOUND AND VIBRATION (2006)
Residual life, predictions from vibration-based degradation signals: A neural network approach
N Gebraeel et al.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS (2004)
Robust performance degradation assessment methods for enhanced rolling element bearing prognostics
H Qiu et al.
ADVANCED ENGINEERING INFORMATICS (2003)
Share Paper
