Performance degradation assessment of rolling bearing cage failure based on enhanced CycleGAN

The accurate degradation performance assessment of rolling bearings is very important for the reliable operation of mechanical equipment. However, most current research is limited to the full life cycle signals of outer ring faults. As a vulnerable part of the rolling bearing, the cage is prone to instantaneous fracture. When the bearing cage fails, its signal amplitude surges in a short time, so this poses a certain challenge compared to the outer ring fault analysis. To solve the problem, an improved CycleGAN model is proposed to generate the full life cycle signals of cage degradation across various measuring points. The dilated convolution is introduced into the generator to further expand the model’s receptive field, which enables the model to capture mutation features of the bearing cage fault signal across multiple scales. And the adaptive learning rate decay strategy is applied in the training process to make the model more focused on fault stage characteristics. Moreover, a deep belief network (DBN) optimized with a grid search algorithm is utilized to fit the root mean square value of the full life cycle signal to characterize the bearing cage degradation rule. The real bearing cage accelerated degradation experiment proves that the proposed model can effectively generate the synthetic signals of different measuring points. Meanwhile, the optimized DBN model can fit the degraded data better than the existing methods.