Intelligent fault diagnosis methods are indispensable for ensuring the reliable operation of industrial equipment. Currently, the growing complexity of intelligent fault diagnostic methods has substantially increased the demand for training data. However, collecting and labeling data from industrial equipment is both time-consuming and costly, leading to challenges associated with small-sample conditions. This limitation poses a significant hurdle to the development of data-driven intelligent fault diagnosis methods. Inspired by the pre-training and fine-tuning paradigms used in large language models, this study proposes a novel generative adversarial fine-tuning (GAFT) framework for bearing fault diagnosis under small-sample conditions. The framework combines generative adversarial network (GAN)-TrAdaBoost and low-rank adaptation (LoRA) fine-tuning to improve data efficiency. GAN-TrAdaBoost employs synthetic data generated through GAN-based data augmentation to provide robust synthetic data support for the pre-trained model. These synthetic data are then fed into the diagnostic model, where TrAdaBoost dynamically adjusts the weights of synthetic data with varying fidelity. Following this, the diagnostic model undergoes fine-tuning using LoRA, which parameterizes weight updates through low-rank decomposition to reduce the ratio of trainable parameters. Comprehensive experiments on two rotational machinery datasets demonstrate that the proposed GAFT consistently outperforms state-of-the-art methods in both diagnostic accuracy and data efficiency. Through the provision of high-fidelity training data and the refinement of model architecture and fine-tuning techniques, this method delivers a scalable and effective framework for intelligent fault diagnosis in bearing systems.
Zhong et al. (Wed,) studied this question.