Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring
作者： T. Warren Liao
DOI: 10.1016/J.ENGAPPAI.2009.09.004
关键词: Feature extraction 、 Grinding wheel 、 Condition monitoring 、 Pattern recognition 、 Dimensionality reduction 、 Feature selection 、 Artificial intelligence 、 Computer science 、 Ant colony optimization algorithms 、 Classifier (UML) 、 Autoregressive model
摘要: … Feature extraction and feature selection are two important … In this study, acoustic emission signals were first collected … for feature extraction, and then the best feature subsets are found …
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