An attention-based stacked BiLSTM framework for predicting remaining useful life of rolling bearings

This publication has 27 references indexed in Scilit:

• Bearing degradation process prediction based on the PCA and optimized LS-SVM model
  Measurement, 2013
• Vibration-based fault diagnosis of a rotor bearing system using artificial neural network and support vector machine
  International Journal of Modelling, Identification and Control, 2012
• Fault diagnosis of ball bearings using continuous wavelet transform
  Applied Soft Computing, 2011
• Fault diagnosis of ball bearings using machine learning methods
  Expert Systems with Applications, 2011
• Current status of machine prognostics in condition-based maintenance: a review
  The International Journal of Advanced Manufacturing Technology, 2010
• A review on machinery diagnostics and prognostics implementing condition-based maintenance
  Mechanical Systems and Signal Processing, 2006
• Residual-life distributions from component degradation signals: A Bayesian approach
  IIE Transactions, 2005
• ARTIFICIAL NEURAL NETWORK BASED FAULT DIAGNOSTICS OF ROLLING ELEMENT BEARINGS USING TIME-DOMAIN FEATURES
  Mechanical Systems and Signal Processing, 2003
• Long Short-Term Memory
  Neural Computation, 1997
• Adapting to Unknown Smoothness via Wavelet Shrinkage
  Journal of the American Statistical Association, 1995