Investigation on Rolling Bearing Remaining Useful Life Prediction: A Review

Rolling bearings are critical components in rotating machinery. Their failure can result in unexpected downtime and productivity reduction. Remaining useful life prediction of rolling bearing has aroused extensive attention, since it can avoid failure risks and improve stability and security of operation. This paper attempts to summarize various methods of bearing remaining useful life prediction which can be roughly classified into three kinds: physical model-based methods, statistical methods and condition monitoring data-driven methods. By comparing the advantages and disadvantages of each kind of these methods, some advice is given for prediction method selection in practical application. This paper is expected to provide a preliminary understanding of various bearing remaining useful life prediction methods.

[1]  M. Farid Golnaraghi,et al.  Prognosis of machine health condition using neuro-fuzzy systems , 2004 .

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

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

[4]  Chao Li,et al.  Machinery condition prediction based on wavelet and support vector machine , 2013, 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE).

[5]  Ratna Babu Chinnam,et al.  A neuro-fuzzy approach for estimating mean residual life in condition-based maintenance systems , 2004 .

[6]  Pavan Kumar Kankar,et al.  A Review on Prognosis of Rolling Element Bearings , 2011 .

[7]  Hong-Zhong Huang,et al.  Support vector machine based estimation of remaining useful life: current research status and future trends , 2015, Journal of Mechanical Science and Technology.

[8]  Ankur Gill.  A Research on Fault Detection and Diagnosis of Rolling Bearing , 2017 .

[9]  S. Marble,et al.  Predicting the remaining life of propulsion system bearings , 2006, 2006 IEEE Aerospace Conference.

[10]  Zhengjia He,et al.  Remaining life prognostics of rolling bearing based on relative features and multivariable support vector machine , 2013 .

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

[12]  Daming Lin,et al.  A review on machinery diagnostics and prognostics implementing condition-based maintenance , 2006 .

[13]  Chiman Kwan,et al.  An integrated approach to bearing fault diagnostics and prognostics , 2005, Proceedings of the 2005, American Control Conference, 2005..

[14]  Gang Niu,et al.  Data-Driven Technology for Engineering Systems Health Management , 2017 .

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

[16]  P. J. Vlok,et al.  Utilising statistical residual life estimates of bearings to quantify the influence of preventive maintenance actions , 2004 .

[17]  Steven Y. Liang,et al.  Adaptive Prognostics for Rolling Element Bearing Condition , 1999 .

[18]  Reza Malekian,et al.  Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review , 2018 .

[19]  T. A. Harris,et al.  A New Stress-Based Fatigue Life Model for Ball Bearings , 2001 .

[20]  Dragan Banjevic,et al.  Calculation of reliability function and remaining useful life for a Markov failure time process , 2006 .

[21]  Rong Li,et al.  Residual-life distributions from component degradation signals: A Bayesian approach , 2005 .

[22]  Andrew K. S. Jardine,et al.  Proportional hazards analysis of diesel engine failure data , 1989 .

[23]  David He,et al.  A segmental hidden semi-Markov model (HSMM)-based diagnostics and prognostics framework and methodology , 2007 .

[24]  Yaoyu Li,et al.  A review of recent advances in wind turbine condition monitoring and fault diagnosis , 2009, 2009 IEEE Power Electronics and Machines in Wind Applications.

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

[26]  P. J. Vlok,et al.  Dynamic residual life estimation of industrial equipment based on failure intensity proportions , 2006 .

[27]  Idriss El-Thalji,et al.  A summary of fault modelling and predictive health monitoring of rolling element bearings , 2015 .

[28]  Steven Y. Liang,et al.  Damage mechanics approach for bearing lifetime prognostics , 2002 .

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

[30]  George Vachtsevanos,et al.  Fault prognosis using dynamic wavelet neural networks , 2001, 2001 IEEE Autotestcon Proceedings. IEEE Systems Readiness Technology Conference. (Cat. No.01CH37237).

[31]  P. M. Anderson,et al.  Application of the weibull proportional hazards model to aircraft and marine engine failure data , 1987 .

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

[33]  Chao Hu,et al.  Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life , 2011, 2011 IEEE Conference on Prognostics and Health Management.

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

[35]  Jin Chen,et al.  Neuro-fuzzy Based Condition Prediction of Bearing Health: , 2009 .

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

[37]  Zhengjia He,et al.  Research on bearing life prediction based on support vector machine and its application , 2011 .

[38]  Dawn An,et al.  Practical options for selecting data-driven or physics-based prognostics algorithms with reviews , 2015, Reliab. Eng. Syst. Saf..

[39]  Yaguo Lei,et al.  A particle filtering-based approach for remaining useful life predication of rolling element bearings , 2014, 2014 International Conference on Prognostics and Health Management.

[40]  Bo-Suk Yang,et al.  Machine health prognostics using survival probability and support vector machine , 2011, Expert Syst. Appl..