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

Abstract

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.

Prognostics and Health Management of Industrial Assets: Current Progress and Road Ahead

“…DTs and RFs have also been applied to fault prognosis, in particular in the contexts of RUL estimation of bearings ( Satishkumar and Sugumaran, 2015 ; Patil et al, 2018 ; Tayade et al, 2019 ), lithium-ion batteries ( Zheng H. et al, 2019 ; Zheng Z. et al, 2019 ) and turbofan engines ( Mathew et al, 2017 ). In Patil et al (2018) , the authors train a RF to perform RUL regression by using time-domain features extracted from the bearings vibration signals. The model is evaluated on the dataset provided by IEEE PHM Challenge 2012 ( Ali et al, 2015 ), showing improved results than previous benchmarks.…”

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“…Furthermore, a set of DTs can be trained and assembled to a Random Forest (RF). According to recent literature on fault diagnosis and prognosis [163][164][165][166][167], RF-based approaches are widely employed due to its low computational cost with large data and stable results.…”

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