A Review on Prognosis of Rolling Element Bearings

Bearings are amongst the frequently encountered components to be found in rotating machinery. Though inexpensive, their failure can interrupt the production in a plant causing unscheduled downtime and production losses. So the bearing prognosis plays a significant role in reducing plant down time and enhanced operation safety, by estimating the Remaining Useful Life (RUL) of damaged bearing. Admitting the importance of bearing prognosis, this literature review attempts to summarize various techniques, methods and models used in the prognosis of bearing till date. The definition of bearing prognosis is discussed in the beginning, followed by classification of various prognostic methods, review of methods used by various investigators in this research domain and concluding the topic with the summary of future research directions.

[1]  A. Palmgren,et al.  Dynamic capacity of rolling bearings , 1947 .

[2]  Shahab Hasanzadeh Ghafari,et al.  A Fault Diagnosis System for Rotary Machinery Supported by Rolling Element Bearings , 2007 .

[3]  K. Goebel,et al.  Fusing competing prediction algorithms for prognostics , 2006, 2006 IEEE Aerospace Conference.

[4]  Joseph Mathew,et al.  Machine Prognostics Based on Health State Estimation Using SVM , 2008, WCE 2009.

[5]  Noureddine Zerhouni,et al.  A mixture of Gaussians Hidden Markov Model for failure diagnostic and prognostic , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[6]  Danilo P. Mandic,et al.  Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability , 2001 .

[7]  Jeremy Sheldon,et al.  Prognostics/diagnostics for gas turbine engine bearings , 2003, 2003 IEEE Aerospace Conference Proceedings (Cat. No.03TH8652).

[8]  Y Shao,et al.  Prognosis of remaining bearing life using neural networks , 2000 .

[9]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using continuous wavelet transform , 2011, Appl. Soft Comput..

[10]  Jay Lee,et al.  Robust performance degradation assessment methods for enhanced rolling element bearing prognostics , 2003, Adv. Eng. Informatics.

[11]  S. Janjarasjitta,et al.  Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal , 2008 .

[12]  H. Toersen,et al.  Condition monitoring of rolling element bearings , 1988 .

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

[14]  Henry C. Pusey An Historical View of the MFPT Society , 1996 .

[15]  Kenneth A. Loparo,et al.  Bearing condition diagnosis and prognosis using applied nonlinear dynamical analysis of machine vibration signal , 2008 .

[16]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[17]  George E. P. Box,et al.  Time Series Analysis: Forecasting and Control , 1977 .

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

[19]  Steven Y. Liang,et al.  Diagnostics and prognostics of a single surface defect on roller bearings , 2000 .

[20]  Ian Howard,et al.  A Review of Rolling Element Bearing Vibration 'Detection, Diagnosis and Prognosis', , 1994 .

[21]  P. K. Kankar,et al.  Rolling element bearing fault diagnosis using autocorrelation and continuous wavelet transform , 2011 .

[22]  Satish C. Sharma,et al.  Fault diagnosis of ball bearings using machine learning methods , 2011, Expert Syst. Appl..

[23]  Guangming Dong,et al.  A hybrid model for bearing performance degradation assessment based on support vector data description and fuzzy c-means , 2009 .