Feature extraction and selection from acoustic emission signals with an application in grinding wheel condition monitoring

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.