Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICARCV.2010.5707866
Title: Continuous health assessment using a single Hidden Markov model
Authors: Geramifard, O.
Xu, J.-X. 
Zhou, J.H.
Li, X.
Keywords: ARMAX
Health condition monitoring
Hidden Markov model
Singular value decomposition
Variance inflation factor
Issue Date: 2010
Source: Geramifard, O.,Xu, J.-X.,Zhou, J.H.,Li, X. (2010). Continuous health assessment using a single Hidden Markov model. 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 : 1347-1352. ScholarBank@NUS Repository. https://doi.org/10.1109/ICARCV.2010.5707866
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. ©2010 IEEE.
Source Title: 11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
URI: http://scholarbank.nus.edu.sg/handle/10635/69720
ISBN: 9781424478132
DOI: 10.1109/ICARCV.2010.5707866
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