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|Title:||Data-driven approaches in health condition monitoring - A comparative study||Authors:||Geramifard, O.
|Issue Date:||2010||Citation:||Geramifard, O.,Xu, J.-X.,Pang, C.K.,Zhou, J.H.,Li, X. (2010). Data-driven approaches in health condition monitoring - A comparative study. 2010 8th IEEE International Conference on Control and Automation, ICCA 2010 : 1618-1622. ScholarBank@NUS Repository. https://doi.org/10.1109/ICCA.2010.5524339||Abstract:||In this paper, four data-driven classification approaches, that is, K-nearest neighbors (K-NN), self-organizing map (SOM), multi-layer perceptron (MLP), and Bayesian Network classifier (BNC), are applied to a health condition monitoring problem for the wearing cutter. The dataset is produced from a cutting machine using force sensing. A genetic algorithm (GA) based search is performed to select 3 dominant features from a 16-dimensional feature space, which is computed directly from the real dataset. Subsequently K-NN, SOM, MLP, and BNC algorithms are trained to predict the wearing status of the cutter, respectively. The suitability of the four data-driven approaches for the health condition monitoring are investigated and compared. © 2010 IEEE.||Source Title:||2010 8th IEEE International Conference on Control and Automation, ICCA 2010||URI:||http://scholarbank.nus.edu.sg/handle/10635/69778||ISBN:||9781424451951||DOI:||10.1109/ICCA.2010.5524339|
|Appears in Collections:||Staff Publications|
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