Tool-Wear Monitoring Based on Continuous Hidden Markov Models

The health status evolving from normal to broken condition of wear tool are needed as an object of assessment in condition-based maintenance (CBM). This paper proposes a continuous Hidden Markov Models (CHMM) to assess the status of the wear tool online based on the normal dataset in the same case. A waveletpackets technology is used to feature extraction and the CHMM is trained by Baum-Welch algorithm. Finally, we compute the log-likelihood based on the trained CHMM for abnormal detection and health assessment in real time during the milling process. Case study on the tool state estimation demonstrates the effectiveness and potential of this methodology.