Fault Diagnosis of Hydraulic Generator Bearing by VMD-Based Feature Extraction and Classification

The vibration signal of hydraulic generator is non-stationary. Features of the early fault signal are weak and thus are difficult to be extracted. In this paper, features of the bearing vibration signal for fault diagnosis are extracted by using the variational mode decomposition (VMD) and singular value. Fault diagnosis is carried out by using the support vector machine (SVM). Firstly, several intrinsic mode functions (IMFs) are obtained by performing VMD on the bearing vibration signal. Then, singular values of the modal component matrix constituted by the intrinsic mode functions are calculated, which are regarded as the feature vector input to the support vector machine. Finally the fault classification and recognition are done by the support vector machine. The proposed method is verified by analyzing the rolling bearing experimental data. The vibration data of the near Wake Island Hydropower Station in Hunan province are used to test the accuracy of the proposed method in practical application.