基于主动生成式过采样和深度堆叠网络的轴承故障诊断

To cope with the class imbalance learning problem, a fault diagnosis method based on active generative o-ver-sampling and Deep Stacking Network (DSN) was proposed. In the training process of an Auxiliary Classifier Generative Adversarial Network (ACGAN), the Wasserstein distance was taken as a new objective function to provide an effective gradient for the generator, and the training times for the generator and discriminator were adaptive-ly adjusted in each iteration to overcome the convergence difficulty caused by their uncoordinated training paces, and thus improve the stability of training ACGAN and the quality of generated samples. A Query By Committee (QBC) based active learning algorithm was used and a Diversity evaluation index was designed to filter the samples that were produced from the AGANN generator and also with high information entropy so as to ensure the diversity of selected samples. At the same time, these filtered samples were utilized to train a discriminator to guide the generator producing the minority samples with rich information. A DSN-based fault classifier was trained from the balanced dataset. A set of comparative experiments were conducted to verify the effectiveness of the proposed method.