A Review of Fault Diagnosis Technology for Rotating Machinery Based on Deep Generative Models

Fault diagnosis of rotating machinery frequently encounters the compounded challenges of non-stationary operating conditions and small-sample data constraints. Traditional deep learning models often exhibit suboptimal performance when addressing these issues. Deep Generative Models (DGMs) offer innovative solutions by synthesizing high-fidelity data samples, reconstructing latent system states, and modeling complex multimodal data streams. This paper presents a review of rotating machinery fault diagnosis technologies based on DGMs. The state-of-the-art applications of DGMs and their variants, including Deep Autoregressive Models (DARs), Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Large Language Models (LLMs), are systematically summarized. Specific applications discussed include data generation, domain adaptation and generalization, multimodal data fusion, as well as downstream fault diagnosis and anomaly detection. Furthermore, this review contrasts DGMs with traditional signal processing methods and standard deep learning models. Current limitations are also critically analyzed, focusing on parameter-efficient fine-tuning strategies, interpretability (reliability), and computational efficiency for edge deployment. Finally, future research directions are proposed, emphasizing few-shot or zero-shot learning, robust multimodal data generation, physics-informed hybrid DGM architectures, and the integration of reinforcement learning with DGMs to further enhance diagnostic accuracy in practical scenarios.