Vibration based Health Assessment of Bearings using Random Forest Classifier

Objective: This paper proposes a predictive model to assess the health condition of bearing using classification technique. Method: In the present study, vibration signals were acquired on a daily basis until the bearing is damaged. Initially, feature selection was done with decision tree and predictive model was built using selected features. Now, Random forest classifier was used to build the model to assess the remaining lifetime of the bearing. Distinct data were used to validate the performance of the classifier. Findings: The classification accuracy of the built model was found to be 95.64%. Applications: The proposed model was tested with the data acquired from a bearing experimental set-up wherein run-to-failure test were conducted on bearings at rated load conditions.