Research on Rolling Bearing Fault Diagnosis Based on Volterra Kernel Identification and KPCA - Yahui Wang; Rong Dong; Xinchao Wang; Xunying Zhang (Shock and Vibration)
A rolling bearing fault diagnosis method based on the Volterra series and kernel principal component analysis (KPCA) is proposed. In the proposed method, first, the improved genetic algorithm (IGA) is used to identify the Volterra series model of the bearing in four states: normal, rolling element fault, inner ring fault, and outer ring fault. The Volterra time-domain kernel is used as the feature vector for kernel principal component analysis to classify and identify the faults. The feasibility of the fault diagnosis method of the Volterra level and kernel principal component analysis is verified by the experimental results.