BearingCog: A Bearing Fault Diagnosis Method under Variable Operational Conditions

Abstract

Rolling bearing is a pivotal component for rotating equipment, which has high failure rates. Bearing failure can cause the equipment to lose control or even casualties, resulting in significant economic losses. This article diagnoses the bearings in variable operational conditions. A novel fault diagnosis framework is proposed to improve the efficiency of fault classification. The variational modal decomposition (VMD) is first utilized to expand the features of the fault signal. Then, principal component analysis (PCA) selects the most representative fault features from the VMD results. After that, the multi-information fusion data is applied to improve the classification accuracy of the support vector machine (SVM). The comparison with respect to some traditional classification methods is illustrated in detail. The diagnostic results show that the proposed framework is a validated tool for diagnosing the bearings.

Keywords

• fault diagnosis
• variable conditions
• feature extraction
• multi-information fusion
• support vector machine