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Bearing remaining useful life estimation based on time–frequency representation and supervised dimensionality reduction

  • Published:2016-05 Issue: Volume:86 Page:41-55
  • ISSN:0263-2241
  • Container-title:Measurement
  • language:en
  • Short-container-title:Measurement

Author:

Zhao Minghang,Tang Baoping,Tan Qian

Funder

National Natural Science Foundation of China

National Key Basic Research and Development Program

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference51 articles.

1. Bearing fault prognostics using Rényi entropy based features and Gaussian process models;Boškoski;Mech. Syst. Signal Process.,2015

2. Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network;Ali;Mech. Syst. Signal Process.,2015

3. Remaining useful life estimation – A review on the statistical data driven approaches;Si;Eur. J. Oper. Res.,2011

4. Machine remaining useful life prediction: an integrated adaptive neuro-fuzzy and high-order particle filtering approach;Chen;Mech. Syst. Signal Process.,2012

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

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