Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport Systems
To address these challenges, we propose a physical-variable-informed deep learning framework (PhyNet) for robust fault diagnosis under real-world sampling heterogeneity.
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Key points
- 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.
- 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.
- These findings demonstrate a deployment-oriented approach to robust diagnosis in heterogeneous industrial environments.
Sources (1)
- [1]Physical-Variable-Informed Deep Learning for Robust Diagnosis in Industrial Fluid Transport SystemsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:12 AM
To address these challenges, we propose a physical-variable-informed deep learning framework (PhyNet) for robust fault diagnosis under real-world sampling heterogeneity.
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.
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