O. Geramifard, J.X. Xu, J.H. Zhou, X. Li, "Multimodal Hidden Markov Model-based Approach for Tool Wear Monitoring", IEEE Trans.on Industrial Electronics, vol. 61, no. 6, pp. 2900-2911, Jun 2014.
In this paper, a novel multi-modal hidden Markov model-based approach is proposed for tool wear monitoring. The proposed approach improves the performance of preexisting hidden Markov model-based approach named physically segmented hidden Markov model with continuous output (PSHMCO) by using multiple PSHMCOs in parallel. In this multi-modal approach, each PSHMCO captures and emphasizes
on a different tool wear regiment. In this article, three weighting schemes, namely, bounded hindsight, discounted hindsight and semi-nonparametric hindsight are proposed and two switching strategies named soft- and hard- switching are introduced to combine the outputs from multiple modes into one. As an illustrative example, the proposed approach is applied to tool wear monitoring in a computer numerically controlled milling machine. The performance of the multi-modal approach with
various weighting schemes and switching strategies is reported and compared with PSHMCO.