Abstract

This paper examines feature selection methods in the context of milling machine tool wear diagnosis. Given raw sensor signals acquired during experiments, a pool of features was created through calculation by several feature extraction methods. Five techniques for selecting the most discriminating features were employed. These techniques included decision trees, neural-fuzzy methods, scatter matrix, and a cross-correlation method. We used a diagnostic neural network to evaluate the five different feature selection schemes by comparing their classification rate and test errors.

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