A novel health indicator for intelligent prediction of rolling bearing remaining useful life based on unsupervised learning model

This publication has 36 references indexed in Scilit:

• Bearing performance degradation assessment based on a combination of empirical mode decomposition and k-medoids clustering
  Mechanical Systems and Signal Processing, 2017
• Deep convolution feature learning for health indicator construction of bearings
  2017
• A recurrent neural network based health indicator for remaining useful life prediction of bearings
  Neurocomputing, 2017
• RUL Prediction of Deteriorating Products Using an Adaptive Wiener Process Model
  IEEE Transactions on Industrial Informatics, 2017
• The application of a general mathematical morphological particle as a novel indicator for the performance degradation assessment of a bearing
  Mechanical Systems and Signal Processing, 2017
• Differential feature based hierarchical PCA fault detection method for dynamic fault
  Neurocomputing, 2016
• An equivalent cyclic energy indicator for bearing performance degradation assessment
  Journal of Vibration and Control, 2014
• Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings
  IEEE Transactions on Industrial Electronics, 2014
• A prognostic method for fault detection in wind turbine drivetrains
  Engineering Failure Analysis, 2014
• Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means
  Mechanical Systems and Signal Processing, 2010