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|Title:||HMM with explicit state duration for prognostics in face milling|
|Keywords:||Hidden Markov model|
Tool condition monitoring
|Source:||Yue, W.,Hong, G.S.,Wong, Y.S. (2010). HMM with explicit state duration for prognostics in face milling. 2010 IEEE Conference on Robotics, Automation and Mechatronics, RAM 2010 : 218-223. ScholarBank@NUS Repository. https://doi.org/10.1109/RAMECH.2010.5513187|
|Abstract:||In this paper, the development of hidden Markov model with explicit state duration (Variable duration HMM) for face milling residual life distribution prognostics is presented. An HMM with explicit state duration is constructed by involving explicit state duration probability. The HMM with explicit state duration offers significant advantages over the conventional HMM in prognostics. The reason why including explicit duration has been both verified theoretically and experimentally in this paper. Moreover, two types of state duration pdf (Gaussian and Weibull distribution) have also been studied. VDHMM based prognostics is demonstrated with the case study which is face milling application. In the case study, the mean residual life calculated from both conventional HMM and VDHMM has been compared with the natural mean residual life. The results of the case study has shown that including the state duration as both Gaussian and Weibull distribution perform better than the conventional HMM. ©2010 IEEE.|
|Source Title:||2010 IEEE Conference on Robotics, Automation and Mechatronics, RAM 2010|
|Appears in Collections:||Staff Publications|
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