A Multiple Model Prediction Algorithm for CNC MachineWear PHM

Abstract

We present a multiple model approach for wear depth estimation of milling machine cutters using dynamometer, accelerometer, and acoustic emission data. The feature selection, initial wear estimation and multiple model fusion components of the proposed algorithm are explained in details and compared with several alternative methods using the training data. The performance  evaluation procedure and the resulting scores from the submitted predictions are also discussed.