Application of support vector machine based on pattern spectrum entropy in fault diagnostics of bearings

Abstract

The fault diagnostics and identification of rolling element bearings have been the subject of extensive research. This paper presents a novel pattern classification approach for the fault diagnostics, which combines the morphological multi-scale analysis and the “one to others” support vector machine (SVM) classifiers. Morphological pattern spectrum describes the shape characteristics of the inspected signal based on the morphological opening operation with multi-scale structuring elements. The pattern spectrum entropy and the barycenter scale location of the spectrum curve are extracted as the feature vectors presenting different faults of the bearings. The “one to others” SVM algorithm is adopted to distinguish six kinds of fault bearing signals which were measured in the experimental test rig running under eight different working conditions. The recognition results of the SVM are ideal even though the training sample is few. The combination of the morphological pattern spectrum parameter analysis and the “one to others” multi-class SVM algorithm is suitable for the on-line automated fault diagnosis of the rolling element bearings. This application is promising and worth well exploiting.