A globally optimized fault diagnosis model based on generative flow model for imbalanced data

Abstract In the actual scenario of fault diagnosis based on deep learning, the diagnosis accuracy is often affected by the lack of fault state data, so the processing of imbalanced data is always a significant challenge. generative adversarial networks (GAN) and denoising diffusion probability models (DDPM) are widely used for data augmentation. However, GAN often shows sensitivity and instability in the training process, and the sample generation speed of DDPM is slow due to the steps requiring multiple iterations–both of which are limiting factors. To solve these problems, we introduce the generative flow network with invertible 1 × 1 convolutions (GLOW) into fault diagnosis. The GLOW model is optimized by maximum likelihood estimation and does not require multiple iterations to generate samples, avoiding the problems faced by GAN and DDPM. In order to generate balanced data explicitly, we propose a condition GLOW (CGLOW) to provide class-balanced samples in real time throughout the framework. On the other hand, using the reversibility of CGLOW, we design an end-to-end fault diagnosis framework that is globally optimized to mitigate the decline in diagnostic accuracy caused by the separation of generation and diagnosis and simplify the steps of fault diagnosis. In addition, to accommodate the non-stationary characteristics of fault signals, we propose a new data transformation method to improve the feature mining ability of the model and the diagnostic accuracy. Finally, we conduct extensive experiments to validate the superiority of the proposed approach. The experimental results demonstrate that our method outperforms existing ones.