Machinability investigation of Inconel 718 in high-speed turning

This article presents an approach based on wavelet correlation modelling for health state monitoring of fluid dynamic bearings in brushless DC motors. This approach involves two stages: (1) extracting of features from the motor-stator current signatures by analysing discrete wavelet transform coefficients; and (2) building of the simplest correlation model between the extracted features and the bearing wear using a multivariable regression technique. The correlation model can be used to detect and predict the bearing wear of brushless DC motors. Experiments were carried out using brushless DC motors with fluid dynamic bearings to verify the proficiency of this approach. Good agreement between the prediction result and the real motor health condition demonstrated the viability of the approach for bearing prognostic applications. The correlation equations obtained have acceptable detectability and accuracy based on a desired 95% level of confidence.