A Domain Adaptation Method Based on Improved Cycle-GAN for Zero-Shot Fault Diagnosis

In the field of rotating machinery fault diagnosis, domain adaptation (DA) methods have emerged as one of the most prevalent approaches for handling cross-scene diagnosis challenges. However, traditional fault diagnosis methods based on DA rely on a large number of unlabeled target-domain samples. In real-world applications, rotating machinery usually produces only healthy signals. The lack of fault samples significantly restricts the application of these methods. To tackle this issue, this paper proposes a novel cycle-consistent generative adversarial network (Cycle-GAN) for data augmentation, named FDCycle-GAN. Specifically, it utilizes all source-domain signals and solely healthy target-domain signals as inputs to generate highly realistic target-domain fault signals. These generated signals are then employed to train the target fault diagnosis network. Moreover, FDCycle-GAN incorporates a series of feature enhancement strategies and introduces semantic consistency and time-frequency consistency constraints. When compared with state-of-the-art domain adaptation methods across three datasets, FDCycle-GAN demonstrates its superiority by achieving higher fault diagnosis accuracy in tasks where fault samples are scarce. This research provides a more practical and efficient solution for rotating machinery fault diagnosis. It holds great significance for safeguarding the stable operation of industrial equipment and minimizing economic losses resulting from equipment malfunctions.