用于动态系统时间序列数据合成的物理信息扩散生成模型
Physics-informed Diffusion Generative Model for Time-Series Data Synthesis in Dynamic Systems
- Beihang University（北京航空航天大学）
- The Hong Kong Polytechnic University（香港理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出PhysDGM模型，将物理规则嵌入扩散生成过程以合成符合物理定律的工业时间序列，构建的440万样本数据集可提升下游任务性能并减少数据需求，助力数据稀缺环境的AI应用。
AI中文摘要：
工业时间序列信号（如航空发动机的涡轮温度和转速）对于监测复杂动态系统的健康状况和运行状态至关重要。然而，这类数据的收集常受恶劣环境（如高温高压）和实验测试成本高昂的限制。为应对这一挑战，我们提出PhysDGM，一种逐步嵌入物理规则的扩散生成模型，用于合成与动态系统 underlying 物理定律一致的时间序列数据。PhysDGM将物理规则直接嵌入生成过程的每一步反向扩散步骤中，确保轨迹级的物理一致性，而非仅在最终输出处施加约束。由PhysDGM构建的大规模AI合成数据集（440万样本，规模扩大20倍）在涵盖涡轮风扇发动机、航空发动机、电池和化学过程的34个数据集上展现出强保真度。纳入合成数据后，下游任务性能大幅超越仅使用真实数据的表现：剩余使用寿命预测提升48%，健康指标估计提升15%，健康状态评估提升22%，故障诊断提升20%。此外，该模型所需训练数据比现有方法少10至20倍，显著降低了动态系统的数据收集成本。我们进一步证明，通过纳入AI合成数据，PhysDGM在识别航空发动机早期故障方面具有潜力。综上，PhysDGM为生成物理一致的工业时间序列提供了坚实基础，为将物理引导的AI扩展到数据稀缺的各类环境（包括工业机械和复杂化学反应动力学）铺平了道路。
英文摘要：
Industrial time-series signals, such as turbine temperature and rotational speed in aero-engines, are essential for monitoring the health and operational status of complex dynamical systems. However, collecting such data is often limited by harsh environments (e.g., high temperature and high pressure) and the high cost of experimental testing. To address this challenge, we introduce PhysDGM, a stepwise physics-embedded diffusion generative model for synthesizing time-series data that are consistent with the underlying physical laws of dynamical systems. PhysDGM embeds physical laws directly into each reverse diffusion step of the generative process, ensuring trajectory-level physical consistency, rather than enforcing constraints only at the final output. A large-scale AI-synthetic dataset (4.4 million samples, 20x scale-up) constructed by PhysDGM demonstrates strong fidelity across 34 datasets spanning turbofan engines, aero-engines, batteries, and chemical processes. After incorporating the synthetic data, the downstream task performance substantially surpassed that using real data alone by 48% for remaining useful life prediction, 15% for health indicator estimation, 22% for state-of-health assessment, and 20% for fault diagnosis. Moreover, it requires 10-20x less training data than existing approaches, substantially reducing the high cost of data collection in dynamical systems. We further demonstrate PhysDGM's potential in identifying early-stage faults in aero-engines by incorporating AI-synthesized data. In summary, PhysDGM provides a solid foundation for generating physically consistent industrial time-series, paving the way for expanding physics-guided AI into diverse data-scarce environments, including both industrial machinery and complex chemical reaction dynamics.
