arXiv (All)
open access
Calibrating Prediction Timeliness Through Multi-Objective Hyperparameter Optimization for Remaining Useful Life Prediction
In predictive maintenance, early and late RUL prediction errors carry asymmetric consequences, yet hyperparameter optimization typically targets a single accuracy metric that treats both directions equally. This study treats the optimization objective itself as a design variable. Five architectures
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
- Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines#620407
- Predictive Maintenance: Deep Learning-Based Remaining Useful Life Prediction for Combat Aircraft Engines#618728
- Predicting Jet Engine Degradation Trajectories for Predictive Maintenance#632058
- A deep learning framework for remaining useful life prediction of turbofan engines with partial sensor failure.#145741
- Aero-engine remaining useful life prediction using state-involving graph networks with multi-scale perception enhancement#435893
Record · ID 1156802
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