Title :
HMM with explicit state duration for prognostics in face milling
Author :
Yue, Wu ; Hong, G.S. ; Wong, Y.S.
Author_Institution :
Mech. Eng., Nat. Univ. of Singapore, Singapore, Singapore
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.
Keywords :
Gaussian distribution; Weibull distribution; hidden Markov models; milling; probability; Gaussian distribution; Weibull distribution; explicit state duration probability; face milling; residual life distribution prognostics; variable duration hidden Markov model; Condition monitoring; Error analysis; Exponential distribution; Gaussian distribution; Handwriting recognition; Hidden Markov models; Mechanical engineering; Milling; Speech recognition; Weibull distribution; hidden Markov model; prognostics; tool condition monitoring;
Conference_Titel :
Robotics Automation and Mechatronics (RAM), 2010 IEEE Conference on
Conference_Location :
Singapore
Print_ISBN :
978-1-4244-6503-3
DOI :
10.1109/RAMECH.2010.5513187