Automatic clustering of vector time-series for manufacturing machine monitoring

Our research in online monitoring of industrial milling tools has focused on the occurrence of certain wide-band transient events. Time-frequency representations of these events appear to reveal a variety of classes of transients, and a time-structure to these classes which would be well modeled using hidden Markov models. However, the identities of these classes are not known, and obtaining a labeled training set based on a priori information is not possible for reasons both theoretical and practical. Unsupervised clustering algorithms which exist are only appropriate for single vector patterns. We introduce an approach to unsupervised clustering of vector series based around the hidden Markov model. This system is justified as a generalization of a common single-vector approach, and applied to a set of vector patterns from a milling data set. Results presented illustrate the value of this approach in the milling application.