Bearing remaining useful life prediction based on deep autoencoder and deep neural networks

Author:

Ren LeiORCID,Sun Yaqiang,Cui Jin,Zhang Lin

Funder

NSFC

National High-Tech Research and Development Plan of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Hardware and Architecture,Software,Control and Systems Engineering

Reference28 articles.

1. Bearing degradation process prediction based on the PCA and optimized LS-SVM model;Dong;Measurement,2013

2. Time–frequency complexity based remaining useful life (RUL) estimation for bearing faults;Singleton;IEEE international symposium on diagnostics for electric machines, power electronics and drives,2013

3. Remaining useful life estimation in rolling bearings utilizing data-driven probabilistic e-support vectors regression;Loutas;IEEE Trans Reliab,2013

4. Estimation of remaining useful life of ball bearings using data driven methodologies;Sutrisno,2012

5. Combined probability approach and indirect data-driven method for bearing degradation prognostics;Caesarendra;IEEE Trans Reliab,2011

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