A novel minority sample fault diagnosis method based on multisource data enhancement

Effective fault diagnosis has a crucial impact on the safety and cost of complex manufacturing systems. However, the complex structure of the collected multisource data and scarcity of fault samples make it difficult to accurately identify multiple fault conditions. To address this challenge, this paper proposes a novel deep-learning model for multisource data augmentation and small sample fault diagnosis. The raw multisource data are first converted into two-dimensional images using the Gramian Angular Field, and a generator is built to transform random noise into images through transposed convolution operations. Then, two discriminators are constructed to evaluate the authenticity of input images and the fault diagnosis ability. The Vision Transformer network is built to diagnose faults and obtain the classification error for the discriminator. Furthermore, a global optimization strategy is designed to upgrade parameters in the model. The discriminators and generator compete with each other until Nash equilibrium is achieved. A real-world multistep forging machine is adopted to compare and validate the performance of different methods. The experimental results indicate that the proposed method has multisource data augmentation and minority sample fault diagnosis capabilities. Compared with other state-of-the-art models, the proposed approach has better fault diagnosis accuracy in various scenarios.