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


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Article Info

DATE

2018-03-16

AUTHORS

Aditya Sharma, M. Amarnath, Pavan Kumar Kankar

ABSTRACT

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. More... »

PAGES

97-114

References to SciGraph publications

  • 2016-04-25. Novel ensemble techniques for classification of rolling element bearing faults in JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
  • 1999-06. Least Squares Support Vector Machine Classifiers in NEURAL PROCESSING LETTERS
  • 2015-05-26. Effect of Unbalanced Rotor on the Dynamics of Cylindrical Roller Bearings in PROCEEDINGS OF THE 9TH IFTOMM INTERNATIONAL CONFERENCE ON ROTOR DYNAMICS
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    http://scigraph.springernature.com/pub.10.1007/s41872-018-0044-x

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    http://dx.doi.org/10.1007/s41872-018-0044-x

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