Fault Diagnosis for Rolling Bearing of Hydroelectric Unit Based on Improved Weighting Domain Adversarial Network and Hybrid Attention Mechanism
Aiming at the problem of insufficient fault samples and unsatisfied diagnosis accuracy,a fault diagnosis method for rolling bearing of hydroelectric unit was proposed in this paper based on improved weighting domain adversarial network(IWDAN)and hybrid attention mechanism(HAM).First,the one-dimensional vibration signal of bearing was transformed into a two-dimensional time-frequency spectrum by the wavelet transform(WT)method,which contributes to characterize the signal in the higher dimensions.Subsequently,in order to ex-tract more efficient shared features between different domains,the time-frequency spectrum in source domain was weighted adaptively using the developed IWDAN model.Finally,the extracted features were served as the inputs of HAM method to effectively suppress the interfer-ence of redundant information and further improve the diagnosis efficiency and accuracy.Based on the case analysis of unit bearing diagno-sis,the superior diagnosis performance of the developed IWDAN-HAM method was validated convincingly,and the corresponding results were helpful to provide reliable data foundation for the formulation of unit maintenance strategies.
万方数据
维普
