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Multi-model diagnostics for various machining conditions: A similarity-based approach

In this paper, a similarity-based multi-model approach based on a pre-existing physically segmented hidden Markov model with continuous output (PSHMCO) is proposed for diagnostics and tool wear monitoring. The proposed approach helps to improve the estimation accuracy in cases that the machinery system undertakes different operating (machining) conditions. The proposed multi-model approach is compared with its single model variant on a tool wear monitoring dataset with various machining conditions. The results indicate that using a similarity function to identify and apply the most similar model out of multiple models can significantly improve the prediction performance compared to blindly utilizing a single general model while adequate training data is available.