Data-driven time-frequency classification techniques applied to tool-wear monitoring

In many pattern recognition applications features are traditionally extracted from standard time-frequency representations (e.g. the spectrogram) and input to a classifier. This assumes that the implicit smoothing of, say, a spectrogram is appropriate for the classification task. It is better to begin with no implicit smoothing assumptions and optimize the time-frequency representation for each specific classification task. Here we describe two different approaches to data-driven time-frequency classification techniques, one supervised and one unsupervised. We show that a certain class of quadratic time-frequency representations will always provide best classification performance. Using our techniques we explore the wear process of milling cutters. Our initial experiments give strong evidence to the nonlinear nature of the wear process and the importance of capturing nonstationary information about each flute-strike to accurately understand the wear process.