A transfer learning strategy based on numerical simulation driving 1D Cycle-GAN for bearing fault diagnosis

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Most transfer learning (TL) models generally need the fault data from similar scenarios to achieve cross-domain bearing fault diagnosis. However, due to the bearings are mostly in normal state and the fault experiment cost is high, it is hard to collect the complete fault data in any real scenario. Thus, the problem of no fault data still limits the application of various intelligent diagnosis methods, including TL methods. To solve this problem, this paper proposed a novel transfer learning (TL) strategy by driving the one-dimensional (1D) cycle-consistent generative adversarial network (Cycle-GAN) with numerical simulation. In this strategy, the bearing dynamic model is constructed to generate the simulated vibration signals under normal and fault states. Furthermore, a 1D Cycle-GAN is first devised as the TL model for signal-to-signal translation. By using all simulated signals (normal and fault) and real normal signals, this 1D Cycle-GAN is trained to translate each simulated signal into a nearly real signal. Two experiments indicate that this strategy can generate satisfactory fault signals to solve the problem of no fault data in bearing fault diagnosis.