Remaining Useful Life Prediction of Bearings Using Ensemble Learning: The Impact of Diversity in Base Learners and Features

[1]  Jay Lee,et al.  Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications , 2014 .

[2]  Michael G. Pecht,et al.  A prognostics and health management roadmap for information and electronics-rich systems , 2010, Microelectron. Reliab..

[3]  Yaguo Lei,et al.  A Model-Based Method for Remaining Useful Life Prediction of Machinery , 2016, IEEE Transactions on Reliability.

[4]  Donghua Zhou,et al.  Remaining useful life estimation - A review on the statistical data driven approaches , 2011, Eur. J. Oper. Res..

[5]  Qing Li,et al.  Degradation Trend Prognostics for Rolling Bearing Using Improved R/S Statistic Model and Fractional Brownian Motion Approach , 2018, IEEE Access.

[6]  Jay Lee,et al.  Intelligent prognostics tools and e-maintenance , 2006, Comput. Ind..

[7]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[8]  G. Meek Mathematical statistics with applications , 1973 .

[9]  Wei Liang,et al.  Dynamic degradation observer for bearing fault by MTS–SOM system , 2013 .

[10]  Ruqiang Yan,et al.  Remaining Useful Life Prediction of Rolling Bearings Using an Enhanced Particle Filter , 2015, IEEE Transactions on Instrumentation and Measurement.

[11]  Raymond J. Mooney,et al.  Creating diversity in ensembles using artificial data , 2005, Inf. Fusion.

[12]  Kai Goebel,et al.  Degradation Modeling and Remaining Useful Life Prediction of Aircraft Engines Using Ensemble Learning , 2018, Journal of Engineering for Gas Turbines and Power.

[13]  Yaguo Lei,et al.  An Improved Exponential Model for Predicting Remaining Useful Life of Rolling Element Bearings , 2015, IEEE Transactions on Industrial Electronics.

[14]  Tianqi Chen,et al.  XGBoost: A Scalable Tree Boosting System , 2016, KDD.

[15]  Steven Y. Liang,et al.  STOCHASTIC PROGNOSTICS FOR ROLLING ELEMENT BEARINGS , 2000 .

[16]  Enrico Zio,et al.  Combining Relevance Vector Machines and exponential regression for bearing residual life estimation , 2012 .

[17]  Yaguo Lei,et al.  Deep convolution feature learning for health indicator construction of bearings , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[18]  Selin Aviyente,et al.  Extended Kalman Filtering for Remaining-Useful-Life Estimation of Bearings , 2015, IEEE Transactions on Industrial Electronics.

[19]  Jianbo Yu,et al.  Bearing performance degradation assessment using locality preserving projections and Gaussian mixture models , 2011 .

[20]  Tangbin Xia,et al.  Recent advances in prognostics and health management for advanced manufacturing paradigms , 2018, Reliab. Eng. Syst. Saf..

[21]  Raymond J. Mooney,et al.  Diverse ensembles for active learning , 2004, ICML.

[22]  Jamie B. Coble,et al.  Merging Data Sources to Predict Remaining Useful Life – An Automated Method to Identify Prognostic Parameters , 2010 .

[23]  Wenjing Jin,et al.  Enhanced Restricted Boltzmann Machine With Prognosability Regularization for Prognostics and Health Assessment , 2016, IEEE Transactions on Industrial Electronics.

[24]  J. Wesley Hines,et al.  Identifying Optimal Prognostic Parameters from Data : A Genetic Algorithms Approach , 2009 .

[25]  Han Tong Loh,et al.  Sequential inspection strategy for multiple systems under availability requirement , 2004, Eur. J. Oper. Res..

[26]  Jin Chen,et al.  Bearing performance degradation assessment based on lifting wavelet packet decomposition and fuzzy c-means , 2010 .

[27]  Marc Lavielle,et al.  Using penalized contrasts for the change-point problem , 2005, Signal Process..

[28]  Nagi Gebraeel,et al.  Residual life predictions from vibration-based degradation signals: a neural network approach , 2004, IEEE Transactions on Industrial Electronics.

[29]  Shaojiang Dong,et al.  Bearing degradation process prediction based on the PCA and optimized LS-SVM model , 2013 .

[30]  Liang Guo,et al.  A recurrent neural network based health indicator for remaining useful life prediction of bearings , 2017, Neurocomputing.

[31]  Đani Juričić,et al.  Bearing fault prognostics using Rényi entropy based features and Gaussian process models , 2015 .

[32]  Trevor Hastie,et al.  Statistical Models in S , 1991 .

[33]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[34]  Chao Hu,et al.  Physics-Based Convolutional Neural Network for Fault Diagnosis of Rolling Element Bearings , 2019, IEEE Sensors Journal.

[35]  Bernhard Schölkopf,et al.  Comparing support vector machines with Gaussian kernels to radial basis function classifiers , 1997, IEEE Trans. Signal Process..

[36]  Bernhard Schölkopf,et al.  The connection between regularization operators and support vector kernels , 1998, Neural Networks.

[37]  Lifeng Xi,et al.  Residual life predictions for ball bearings based on self-organizing map and back propagation neural network methods , 2007 .

[38]  Ruqiang Yan,et al.  Bearing Degradation Evaluation Using Recurrence Quantification Analysis and Kalman Filter , 2014, IEEE Transactions on Instrumentation and Measurement.

[39]  Fritz Klocke,et al.  Material Removal Mechanisms in Lapping and Polishing , 2003 .

[40]  Thomas G. Habetler,et al.  A survey of condition monitoring and protection methods for medium voltage induction motors , 2009, 2009 IEEE Energy Conversion Congress and Exposition.

[41]  Dazhong Wu,et al.  An ensemble learning-based prognostic approach with degradation-dependent weights for remaining useful life prediction , 2017, Reliab. Eng. Syst. Saf..

[42]  Charles L. Lawson,et al.  Solving least squares problems , 1976, Classics in applied mathematics.

[43]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[44]  Noureddine Zerhouni,et al.  A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling , 2013, 2013 IEEE Conference on Prognostics and Health Management (PHM).

[45]  Vladimir Vapnik,et al.  Support-vector networks , 2004, Machine Learning.

[46]  Kwok-Leung Tsui,et al.  Statistical Modeling of Bearing Degradation Signals , 2017, IEEE Transactions on Reliability.

[47]  Tommy W. S. Chow,et al.  Anomaly Detection and Fault Prognosis for Bearings , 2016, IEEE Transactions on Instrumentation and Measurement.

[48]  Brigitte Chebel-Morello,et al.  Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network , 2015 .