Few-shot fault identification of complex equipment via metric-based features capture GAN combining prior knowledge-augmented strategy

In engineering, complex equipment during aerospace engine manufacturing fails to carry out large-scale fault simulations, which leads to limited fault signals collected, resulting in poor efforts at intelligent fault identification for equipment. In existing research, generative adversarial network (GAN) is considered to be an efficient solution to such issues. However, few-shot data and conventional generation mechanisms may lead to time-consuming training and subpar quality of the generated samples. To overcome these challenges, we propose metric-based features capture GAN combining prior knowledge-augmented strategy that applies a new generation mechanism. The network is made up of a feature sampler, a generator, a discriminator, and an assistant classifier. The feature sampler provides the potential features of the few-shot data to assist in the training of the generator, which can significantly reduce the learning difficulty of the network. Furthermore, to accelerate model convergence, a feature metric module is inserted into the generator to compel the model to achieve knowledge transfer. By conducting experimental analysis on three bearing failure datasets, the proposed scheme has been confirmed to be efficacious and superior. In addition, ablation experiments indicate that the proposed scheme is valid for reducing the time-consuming training of GAN.