Life assessment and health monitoring of rolling element bearings: an experimental study

Rolling element bearings are the key elements of almost all rotating machineries and play a major role in efficient performance of such machines. Industries are facing difficulties to develop a reliable methodology that can help to predict the remaining useful life of such machine elements, so that these can be replaced before catastrophic failure. This study proposes a data-driven framework for predicting the remnant life of bearings, based on nonlinear dimensionality reduction and least-square support vector regression. Experiments are conducted to assess the life of bearing. Vibration signals are extracted from the test bearing and various time and frequency domain features are used to form a health index. This health index is then used for learning and training the regression model which helps in assessment of remaining useful life. Vibration parameters monitoring and wear mechanisms observations have been carried out to identify the various degradation stages of the bearing. Results show the potential of proposed methodology for predicting the remaining useful life of the bearing.

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