A Novel Method Based on Stepwise Variational Modal Decomposition and Gramian Angular Difference Field for Bearing Health Monitoring

The health status of bearings seriously affects the operational efficiency of equipment, and it is important to carry out bearing health status detection. A bearing fault diagnosis method based on stepwise variational modal decomposition (SVMD) with adaptive initialization center frequency and Gramian angular difference field is proposed. Firstly, a method of center frequency initialization base on frequency energy distribution characteristics is proposed to improve the decomposition speed and stability. Secondly, SVMD with single component decomposition and local decomposition is proposed to improve decomposition efficiency. It can effectively avoid inconsistency in different signal parameter settings and ensures consistency in the number of signal components, which is very suitable for batch processing of signals. Finally, Gramian angular field (GAF) and convolutional neural networks (CNNs) are combined to extract features of the reconstructed signal spectrum and enhance the differential characteristics between different signal spectrum. The experiment shows that the center frequency initialization method can shorten the single decomposition time from 11.13 to 6.71 s. The overall recognition rate can reach 95.2%, which is at least 1.9% higher than other decomposition methods.