A virtual sensing based augmented particle filter for tool condition prognosis

Timely evaluation and prediction of tool condition is critical to establish optimized maintenance plans in order to enhance production, minimize costly downtime. This paper presents an augmented particle filter based on virtual sensing technique with support vector regression (SVR) model to account for uncertainties in the tool condition degradation process. Tool condition is predicted by recursively updating a physics-based tool condition degradation model with virtual measurement approximately estimating tool degradation condition through virtual sensing technique, following a Bayesian inference scheme. Additionally, in order to improve estimation accuracy of virtual sensing model, different state-of-the-art dimension reduction techniques including principal component analysis (PCA) and its kernel version (KPCA), locality preserving projection (LPP) method have been investigated for feature fusion in a virtual sensing model, and the KPCA method performs best in terms of sensing accuracy. Afterwards, virtual measurement is then incorporated into particle filter. The effectiveness of the developed method is experimentally validated in a set of machining tool run-to-failure tests on a computer numerical control (CNC) milling machine.