Tool wear monitoring by machine learning techniques and singular spectrum analysis

This paper explores the use of data mining techniques for tool condition monitoring in metal cutting. Pseudo-local singular spectrum analysis (SSA) is performed on vibration signals measured on the toolholder. This is coupled to a band-pass filter to allow definition and extraction of features which are sensitive to tool wear. These features are defined, in some frequency bands, from sums of Fourier coefficients of reconstructed and residual signals obtained by SSA. This study highlights two important aspects: strong relevance of information in high frequency vibration components and benefits of the combination of SSA and band-pass filtering to get rid of useless components (noise).