arXiv (All)
open access
Bi-directional digital twin prototype anchoring with multi-periodicity learning for few-shot fault diagnosis
Intelligent fault diagnosis (IFD) typically requires abundant labeled data, which are difficult to obtain from physical assets in practical industrial applications. Digital twins (DTs) provide a promising alternative by generating labeled simulation data, while most existing DT-assisted diagnosis me
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
- Fault Diagnosis Method for Mechanical Components Fusing RPM, DAM-IResNet, and Transfer Learning.#9341
- System-aware contextual digital twin for ICS anomaly diagnosis#138846
- Attestream: Usage-Aware Intermittent Data Distribution with Verifiable Lifecycle Provenance for Machine-Learning Data Streams#984550
- Ground-Truth-Aware Stress Testing of a Closed-Loop Smart-Building Digital Twin Under Sensor Drift and Missing Data#609332
- Field testing of low-cost particulate matter sensors for Digital Twin applications in nanomanufacturing processes#1169766
Record · ID 1536093
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