Roller Bearing Defect Prognosis using Likelihood Parameters and Proportional Hazards Model
Volume 6, Number 5, September 2010 - Paper 2 - pp. 425-434
A. K. VERMA1, B. SREEJITH2 and A. SRIVIDYA31 Department of Electrical Engineering,
2 Interdisciplinary Programme in Reliability Engineering,
3 Department of Civil Engineering,
Indian Institute of Technology Bombay, Mumbai - 400076, India.
(Received on August 31, 2009, revised on March 17, 2010)
Bearings are critical components employed virtually in all rotating machines and automobiles to alleviate friction between surfaces during relative motion. In traditional approaches, rolling element bearing failures are predicted based on either historical time-to-failure data (event data) or condition monitoring (CM) data. Prediction methods using event data are of little value to maintenance decision making since they render general forecasts for the total population of identical units instead of forecast for a particular unit presently operating in the machine. Prognosis based on CM data provides short term predictions which may not be useful in maintenance scheduling. Proportional hazards model (PHM) can be used to predict hazard rates and reliability of machines and its components using both event data and CM data.
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