Euclidean distance based feature ranking and subset selection for bearing fault diagnosis

Abstract

Bearing failure can cause hazardous effects on rotating machinery. The diagnosis of the fault is very critical for reliable operation. The main steps for the machine learning process involve feature extraction, selection, and classification. Feature selection contains an identification of noble features that performs for better classification accuracy with fewer features and with less computational time. For a large feature dimension; a critical study is required to catch the best feature subset for proper diagnosis. So, this paper presents a unique feature ordering and selection technique called Feature Ranking and Subset Selection based on Euclidean distance (FRSSED). Two bearing databases have considered for verification of the robustness of the proposed technique. One database was obtained from the experiment, and the other publicly available database was collected from Case Western Reserve University (CWRU). Initially, the vibration signals have captured from bearings having an individual as well as combined defects in various components along with healthy bearing. EEMD was applied to these signals, and then, the sensitive IMF was selected by the envelope spectrum. In the later stage, the feature extraction was carried out from the selected IMF using fifteen statistical features. Afterward, the extracted features were introduced into FRSSED algorithm for feature ordering. These ordered features were fed into various classifiers. The comparison was made for classification accuracy and time consumption among generalized method (without feature ordering), principal component analysis (PCA), and FRSSED. The diagnostic outcomes describe that the suggested feature reduction technique improves the classification accuracy with fewer feature subset along with considerable time-saving.