PhyNet框架让工业流体系统故障诊断不受采样差异影响
Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems
搞工业故障诊断的可以看看，PhyNet 用物理变量当约束，四个基准包括三个真实空化数据集都拿了最优，不要求信号时间对齐，落地友好。
arXiv 论文提出物理变量感知的深度学习框架 PhyNet，用于工业流体传输系统的故障诊断。该方法把物理变量作为全局运行约束，把响应信号作为局部诊断证据，通过 physical-to-response cross-attention 机制实现无需严格时间对齐的交互。在包括三个真实空化数据集和一个公开两相流数据集在内的四个基准上，PhyNet 在所有评估指标上取得最优成绩。注意力与掩码分析显示物理变量有效约束了局部响应信号的解读，提升了模糊工况的区分能力。
Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems
Industrial fluid transport systems are monitored through heterogeneous sensing streams, but robust fault diagnosis remains difficult because response signals vary with operating conditions and differ from physical variables in sampling rate, observation duration and data granularity. To address these challenges, we propose a physical-variable-informed deep learning framework (PhyNet) for robust fault diagnosis under real-world sampling heterogeneity. PhyNet treats physical variables as global operating constraints and response signals as local diagnostic evidence. Its physical-to-response cross-attention mechanism connects sequence-level physical-variable tokens with a learnable global response token, enabling interaction without strict temporal alignment. Across four benchmarks, including three real-world cavitation datasets and one public two-phase-flow dataset, PhyNet achieves the best performance across all evaluated metrics. Class-wise analyses, together with attention and masking analyses, show improved discrimination of ambiguous states and indicate that physical variables meaningfully constrain local response interpretation. These findings demonstrate a deployment-oriented approach to robust diagnosis in heterogeneous industrial environments.