Tool life prediction based on Gauss importance resampling particle filter

Abstract

Effective tool remaining useful life (RUL) prediction can greatly improve the quality of workpiece and reduce the cost of production. An online tool RUL prediction framework is constructed based on Bayesian inference and sensory signals. In this method, Pairs model is adopted to depict the tool degradation process during cutting process and Gaussian importance resampling (GIR) method is proposed to update model parameters iteratively. Therefore, the future tool wear status can be predicted and RUL can be estimated correspondingly. To testify the effectiveness of the proposed method, milling experiment was carried out and relative waveform features are extracted to depict the relationship between sensory signal and tool wear value. The analysis and comparison show that Gaussian importance resampling method can avoid the problem of particle degradation and impoverishment effectively so as to realize accurate RUL prediction.