Multi-rate hidden Markov models and their application to machining tool-wear classification

The paper introduces a multi-rate hidden Markov model (multi-rate HMM) for multi-scale stochastic modeling of non-stationary processes. The multi-rate HMM decomposes the process variability into scale-based components, and characterizes both the intra-scale temporal evolution of the process and the inter-scale interactions. Applying these models to the machine tool-wear classification problem in a titanium milling task shows that multi-rate HMMs outperform HMMs in terms of both accuracy and confidence of predictions.

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