Continuous health assessment using a single hidden Markov model

Author

Geramifard, O. ; Xu, J.X. ; Zhou, J.H. ; Li, X.

Author_Institution

Electr. & Comput. Eng. Dept., Nat. Univ. of Singapore, Singapore, Singapore

fYear

2010

fDate

7-10 Dec. 2010

Firstpage

1347

Lastpage

1352

Abstract

In this paper, two temporal models, Hidden Markov Model and Auto Regressive Moving Average model with exogenous inputs (ARMAX), are used for health condition monitoring of the cutter in a milling machine. Dataset is acquired through real time force signal sensing. A heuristic statistical approach is used to select dominant features, leading to the selection of 3 dominant features from the 16-dimensional feature space. Subsequently Hidden Markov Model and ARMAX model have been trained to predict the wearing status of the cutter in the milling machine. Suitability of these approaches are investigated and compared.

Keywords

autoregressive moving average processes; condition monitoring; cutting tools; hidden Markov models; milling machines; wear; ARMAX; autoregressive moving average model-with-exogenous inputs; cutter; health assessment; health condition monitoring; heuristic statistical approach; milling machine; single-hidden Markov model; wearing status; Autoregressive processes; Condition monitoring; Feature extraction; Force; Hidden Markov models; Predictive models; Training; ARMAX; Health Condition Monitoring; Hidden Markov Model; Singular value decomposition; Variance Inflation Factor;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Automation Robotics & Vision (ICARCV), 2010 11th International Conference on

Conference_Location

Singapore

Print_ISBN

978-1-4244-7814-9

Type

conf

DOI

10.1109/ICARCV.2010.5707866

Filename

5707866