An Efficient Incremental Learning of Bearing Fault Imbalanced Data Set via Filter StyleGAN

Abstract

At present, the bearing fault is one of the major mechanical faults, deep learning-based bearing fault diagnosis methods have been successfully developed. However, the fault data of bearings in the industrial environment are very few, which leads to the failure or performance degradation of conventional intelligent diagnosis methods. It costs a large amount of manpower and material to obtain complete samples of the industrial environment, which is unrealistic. Therefore, it is necessary to use a small number of actual fault signals for incremental learning. The existing incremental learning models cannot solve the entanglement problem of sample features, and the ability to obtain new samples by combining features is limited. In this article, the filter style-based generative adversarial network (FSGAN) is used to separate hidden features by mapping data features to intermediate latent space, and then, new signals are generated by feature reorganization. The different signals are generated by controlling the weight coefficient of style after network training, and then, valuable signals are selected by the t-distributed stochastic neighbor embedding (t-SNE) clustering analysis. The model can generate fault signals with unknown sizes under unknown conditions. Finally, the advantages of FSGAN are further verified by the quality evaluation of the generated signals and comparison with other models.