Mechanical system monitoring using hidden Markov models

A hidden Markov model (HMM)-based approach to mechanical system monitoring is presented. The resulting system is shown to be useful for machining applications with the associated problems of tool wear detection and prediction. The approach is based on continuous density, left-right HMMs that closely match the one-way, fresh-to-worn transition process of machining tools. The Baum-Welch iterative training procedure is modified to incorporate prior knowledge of the transitions between tool wear states. Results presented demonstrate that a multisensor HMM-based system is an effective approach for tool wear detection and prediction