An Instance and Feature-Based Hybrid Transfer Model for Fault Diagnosis of Rotating Machinery With Different Speeds

Recently, transfer learning approaches using pre-trained models to learn new tasks enable many real-world applications. However, when detecting rotating machinery faults under different conditions like rotating speeds, classical instance-based transfer (IBT) algorithms represented by generative adversarial networks (GANs) require the source and target domains to have similar feature spaces. Meanwhile, the other feature-based transfer (FBT) algorithms often face feature extraction difficulty and domain over-adaptation. To overcome these problems, a novel hybrid transfer model that combines both ideas of IBT and FBT algorithms is proposed in this article. This hybrid model first introduces a cycle-consistent GAN (CycleGAN) to generate labeled pseudo samples. By adding an operating-condition discriminator and a fault-consistency discriminator to the CycleGAN, generated samples are easier to transfer operating conditions and retain the domain-invariant fault information. Second, generated and target samples are sent to a deep domain confusion (DDC)-based network to adapt feature domain spaces and further predict the labels of target samples. Compared with the traditional IBT or FBT model, the proposed hybrid transfer model can combine both advantages of these two competitive models, broadening the transferable domain and improving task accuracy. In addition, a pseudo-label-based transition strategy for labeling intermediate domain samples is also proposed to help the transfer model perform better when unlabeled intermediate domain samples are available. Lab experiments verify that the proposed hybrid model and transition strategy can significantly improve the fault diagnosis accuracy of rotating machinery with different speeds.