Life Prediction of Bearing by Using Adaboost Regressor

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 complex boosting ensemble like AdaBoost Regressor for prediction of RUL with time-domain features which are extracted by applying various statistical functions on given vibration signals. Decision based Feature Ranking Method is used to find the contribution of each feature. 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. In experimental analysis, the proposed approach is verified using FEMTO bearing data-set provided by IEEE PHM Data Challenge 2012. Weibull Hazard Rate Function for each bearing from learning data set are used to find target values i.e. projected RUL of the bearings. Ensemble models are trained using extracted time domain features and target values of learning data set which contains vibration signals of six run-to-failure bearings with three different combinations of speed and load. Vibration data set of eleven partially degraded bearings from same three groups are fed as testing data set to trained models to estimate their RULs. Appropriate scoring function is used to calculate score of models using error between given real RULs and predicted values of test set bearings. Testing scores 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.

Keywords: RUL prediction, Vibration signals, Regression model