Journal
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
Volume 23, Issue 1, Pages 74-84Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.engappai.2009.09.004
Keywords
Acoustic emission; Wheel condition monitoring; Feature extraction; Autoregressive model; Discrete wavelet decomposition; Feature selection; Ant colony optimization; Sequential forward floating selection
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Feature extraction and feature selection are two important issues in sensor-based condition monitoring of any engineering systems. In this study, acoustic emission signals were first collected during grinding operations, next processed by autoregressive modeling or discrete wavelet decomposition for feature extraction, and then the best feature subsets are found by three different feature selection methods, including two proposed ant colony optimization (ACO)-based method and the famous sequential forward floating selection method. Posing monitoring as a classification problem, the evaluation is carried out by the wrapper approach with four different algorithms serving as the classifier. Empirical test results were shown to illustrate the effectiveness of feature extraction and feature selection methods. (C) 2009 Elsevier Ltd. All rights reserved.
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