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.
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