Rolling Bearing Fault Diagnosis in Limited Data Scenarios Using Feature Enhanced Generative Adversarial Networks

Most current rolling bearing fault diagnostic approaches presume that sufficient labeled data are available for training. However, labeled fault data collection in realistic engineering is time-consuming and expensive, whereas the majority of data available are unlabeled, restricting diagnostic performance. To alleviate the dependence of feature self-extraction model on rolling bearing fault data ...