arXiv (All)
open access
A Multi-level Information Integration Framework for Physically Verifiable Fault Diagnosis of Rotating Machinery
Integrating multi-level information, from physical models through data-driven diagnostics to natural language reasoning, into verifiable decision chains is a growing need in intelligent manufacturing. In bearing fault diagnosis, taken here as a representative testbed, the standard output is a class
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
- Digital-Twin-Driven Predictive Maintenance and Fault-Tolerant Control for Electrified Agricultural Machinery: A Multiphysics and Deep Reinforcement Learning Framework for PMSM In-Wheel Drives#707434
- Multimodal semantic learning for early fault diagnosis in warp knitting machines via masked fault modeling.#645626
- From Anomalies to Failures: Constructing Causal Error Graphs for Agentic Trace Diagnosis#1116845
- Agriculture and Food in the Era of Generative Artificial Intelligence: Constructing the AIFE-SC (Artificial Intelligence–Food–Ecology Symbiosis Cycle) Theory#728352
- Skeleton-Aware Artificial Intelligence: Implementing Structural Negentropy for Cross-Domain Automated Theorem Proving#170057
Record · ID 1076113
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