arXiv (OAI Expanded)
open access
Deep Learning-Based Detection of Electrical Faults and Power Quality Disturbances in Aerospace Power Systems
More Electric Aircraft require fast and reliable monitoring of high-frequency electrical networks, yet most power quality disturbance and fault diagnosis methods are developed for conventional 50 or 60 Hz grids. This work presents a hardware-aware deep learning framework for multiclass detection of
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
- Power Transformer Fault Classification Using Industry 4.0-Compliant Machine Learning Under Data Missing Conditions#445025
- LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting: Deep Learning for Renewable Energy Demand Prediction.#489716
- Hybrid deep learning for smart grid SCADA intrusion detection with mutual information feature selection for smart meter data analysis#824317
- Robust Industrial Cyber Physical Classification Using Neuromorphic Temporal Embeddings and Hybrid SNN XGBoost Under Machine Unlearning Attacks#987302
- Deep learning-based stacked models for cyber-attack detection in industrial internet of things#340101
Record · ID 1070947
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