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Remaining Useful Life (RUL) Prediction of Rolling Element Bearing Using Random Forest and Gradient Boosting Technique

[+] Author Affiliations
Sangram Patil, Aum Patil, Vishwadeep Handikherkar, Sumit Desai, Vikas M. Phalle, Faruk S. Kazi

V.J.T.I., Mumbai, India

Paper No. IMECE2018-87623, pp. V013T05A019; 7 pages
  • ASME 2018 International Mechanical Engineering Congress and Exposition
  • Volume 13: Design, Reliability, Safety, and Risk
  • Pittsburgh, Pennsylvania, USA, November 9–15, 2018
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5218-7
  • Copyright © 2018 by ASME


Rolling element bearings are very important and highly utilized in many industries. Their catastrophic failure due to fluctuating working conditions leads to unscheduled breakdown and increases accidental economical losses. Thus these issues have triggered a need for reliable and automatic prognostics methodology which will prevent a potentially expensive maintenance program. Accordingly, Remaining Useful Life (RUL) prediction based on artificial intelligence is an attractive methodology for several researchers. In this study, data-driven condition monitoring approach is implemented for predicting RUL of bearing under a certain load and speed. The approach demonstrates the use of ensemble regression techniques like Random Forest and Gradient Boosting for prediction of RUL with time-domain features which are extracted from given vibration signals. The extracted features are ranked using Decision Tree (DT) based ranking technique and training and testing feature vectors are produced and fed as an input to ensemble technique. Hyper-parameters are tuned for these models by using exhaustive parameter search and performance of these models is further verified by plotting respective learning curves. For the present work FEMTO bearing data-set provided by IEEE PHM Data Challenge 2012 is used. Weibull Hazard Rate Function for each bearing from learning data set is used to find target values i.e. projected RUL of the bearings. Results of proposed models are compared with well-established data-driven approaches from literature and are found to be better than all the models applied on this data-set, thereby demonstrating the reliability of the proposed model.

Copyright © 2018 by ASME



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