Condition and structural health monitoring (CM/SHM) is a pivotal component of predictive maintenance (PdM) strategies across diverse industrial sectors, including mechanical rotating machinery, aircraft structures, wind turbines, and civil infrastructures. Conventional deep learning models, while effective for fault diagnosis and anomaly detection through automatic feature learning from sensor data, often struggle with operational variability, imbalanced or scarce fault datasets, and multimodal sensory data from complex systems. Deep generative models (DGMs) including deep autoregressive models, variational autoencoders, generative adversarial networks, diffusion-based models, and emerging large language models, offer transformative capabilities by synthesizing high-fidelity data samples, reconstructing latent system states, and modeling complex multimodal data streams. This review systematically examines state-of-the-art DGM applications in CM/SHM across the four main industrial systems mentioned above, emphasizing their roles in addressing key challenges: data generation, domain adaptation and generalization, multimodal data fusion, and downstream fault diagnosis and anomaly detection tasks, with rigorous comparison among signal processing, conventional machine learning or deep learning models, and DGMs. Lastly, we discuss current limitations of DGMs, including challenges of explainable and trustworthy models, computational inefficiencies for edge deployment, and the need for parameter-efficient fine-tuning strategies. Future research directions can focus on zero-shot and few-shot learning, robust multimodal data generation, hybrid architectures integrating DGMs with physics knowledge, and reinforcement learning with DGMs to enhance robustness and accuracy in industrial scenarios.

翻译：状态与结构健康监测（CM/SHM）是机械旋转设备、飞机结构、风力涡轮机及民用基础设施等众多工业领域预测性维护（PdM）策略的关键组成部分。传统深度学习模型虽能通过从传感器数据中自动学习特征来实现故障诊断与异常检测，但在应对运行变异性、不平衡或稀缺的故障数据集以及复杂系统的多模态传感数据方面常面临困难。深度生成模型（DGMs）——包括深度自回归模型、变分自编码器、生成对抗网络、基于扩散的模型以及新兴的大语言模型——通过合成高保真数据样本、重构潜在系统状态及建模复杂多模态数据流，提供了变革性的能力。本综述系统性地考察了上述四大工业系统中DGMs在CM/SHM领域的前沿应用，重点阐述了其在应对关键挑战中的作用：数据生成、领域自适应与泛化、多模态数据融合以及下游故障诊断与异常检测任务，并对信号处理方法、传统机器学习或深度学习模型与DGMs进行了严格比较。最后，我们探讨了DGMs当前存在的局限性，包括可解释与可信赖模型的挑战、边缘部署的计算效率问题以及对参数高效微调策略的需求。未来的研究方向可聚焦于零样本与少样本学习、鲁棒的多模态数据生成、融合DGMs与物理知识的混合架构，以及结合强化学习的DGMs方法，以提升工业场景下的鲁棒性与准确性。