arXiv (All)
open access
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 thes
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Causally-Constrained Probabilistic Forecasting for Time-Series Anomaly Detection#1354885
- Robust Fault Detection in Mechanical Multimodal Time Series via Self-Supervised Cross-Modal Reconstruction#1110679
- Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks#987302
- Construction of an Automated Quality Control Model for Industrial Waste Gas Online Monitoring Data Based on Unsupervised Learning#689997
- A Novel Hybrid Dual-Stream Deep Learning Architecture Integrating Multi-State LSTM, CNN, and Multi-Head Self-Attention for Water Level and Discharge Prediction (Missouri River Basin, USA)#1081850
Record · ID 1411866
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive