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|Title:||Continuous health assessment using a single Hidden Markov model||Authors:||Geramifard, O.
Health condition monitoring
Hidden Markov model
Singular value decomposition
Variance inflation factor
|Issue Date:||2010||Citation:||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|
|Appears in Collections:||Staff Publications|
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