Feature-based performance of SVM and KNN classifiers for diagnosis of rolling element bearing faults

Rolling element bearings (REBs) are vital parts of rotating machinery across various industries. For preventing breakdowns and damages during operation, it is crucial to establish appropriate techniques for condition monitoring fault diagnostics these bearings. The development machine learning (ML) brings a new way diagnosing the rolling In current work, ML models, namely, Support Vector Machine (SVM) K-Nearest Neighbor (KNN), used classify faults associated with different ball bearing elements. Using open-source Case Western Reserve University (CWRU) data, classifiers trained extracted time-domain frequency-domain features. results show that features more convincing training KNN classifier has high level accuracy compared SVM.