arXiv cs.AI· Yu Sha, Junqi Tao, Dixin Zhou, Horst Stoecker, Domagoj Vnucec, Andreas Widl, Kai Zhou·· 12 小时前AI 评分19
PhyNet：面向工业流体输送系统鲁棒故障诊断的物理变量信息深度学习框架
Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems
AI 导读
研究者提出物理变量信息深度学习框架 PhyNet，将物理变量作为全局运行约束、响应信号作为局部诊断证据，通过物理到响应的交叉注意力机制连接序列级物理变量 token 与可学习的全局响应 token，无需严格时间对齐。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 在所有评估指标上均取得最佳性能。
正文 · 原文
Abstract: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.
| Comments: | 29 pages, 5 figures |
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.27891 [cs.LG] |
| (or arXiv:2610.27891v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.27891 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yu Sha [view email]
[v1]
Tue, 20 Oct 2026 09:12:32 UTC (15,188 KB)
来源：arXiv cs.AI · arxiv.org