Distributed agents structure for current-only adaptive relaying scheme reinforced against failures and cyberattacks DOI Creative Commons
Mohamed Elgamal, Amir Abdel Menaem, Majed A. Alotaibi

et al.

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 103143 - 103143

Published: Nov. 1, 2024

Language: Английский

IntDEM: an intelligent deep optimized energy management system for IoT-enabled smart grid applications DOI

P. Ganesh,

B. Meenakshi Sundaram, Praveen Kumar Balachandran

et al.

Electrical Engineering, Journal Year: 2024, Volume and Issue: unknown

Published: July 24, 2024

Language: Английский

Citations

4
A robust deep learning system for motor bearing fault detection: leveraging multiple learning strategies and a novel double loss function DOI
Khoa D. Tran,

Lam Pham,

Nguyễn Văn Anh

et al.

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(4)

Published: Feb. 20, 2025

Language: Английский

Citations

0
A Deep Learning-Based Cyberattack Detection Method for Line Differential Relays DOI
Mohamed Elgamal, Abdelfattah A. Eladl, Bishoy E. Sedhom

et al.

Internet of Things, Journal Year: 2025, Volume and Issue: unknown, P. 101574 - 101574

Published: March 1, 2025

Language: Английский

Citations

0
Advancements in Grid Resilience: Recent Innovations in AI-Driven Solutions DOI Creative Commons

Sana Hafez,

Mohammad Alkhedher, Mohamed Ramadan

et al.

Results in Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 105042 - 105042

Published: April 1, 2025

Language: Английский

Citations

0
Explainable hybrid forecasting model for a 4-node smart grid stability DOI
Taher M. Ghazal, Mohammad Kamrul Hasan, Rosilah Hassan

et al.

Energy Reports, Journal Year: 2025, Volume and Issue: 13, P. 4948 - 4961

Published: April 24, 2025

Language: Английский

Citations

0
A Robust Deep Learning System for Motor Bearing Fault Detection: Leveraging Multiple Learning Strategies and a Novel Double Loss Function DOI Creative Commons
Khoa D. Tran,

Lam Pham,

Nguyễn Văn Anh

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 30, 2024

Abstract Motor bearing fault detection (MBFD) is vital for ensuring the reliability and efficiency of industrial machinery. Identifying faults early can prevent system breakdowns, reduce maintenance costs, minimize downtime. This paper presents an advanced MBFD using deep learning, integrating multiple training approaches: supervised, semi-supervised, unsupervised learning to improve classification accuracy. A novel double-loss function further enhances model’s performance by refining feature extraction from vibration signals. Our approach rigorously tested on well-known datasets: American Society Mechanical Failure Prevention Technology (MFPT), Case Western Reserve University Bearing Data Center (CWRU), Paderborn University's Condition Monitoring Damage in Electromechanical Drive Systems (PU). Results indicate that proposed method outperforms traditional machine models, achieving high accuracy across all datasets. These findings underline potential applying MBFD, providing a robust solution predictive settings supporting proactive management machinery health.

Language: Английский

Citations

0
Distributed agents structure for current-only adaptive relaying scheme reinforced against failures and cyberattacks DOI Creative Commons
Mohamed Elgamal, Amir Abdel Menaem, Majed A. Alotaibi

et al.

Ain Shams Engineering Journal, Journal Year: 2024, Volume and Issue: unknown, P. 103143 - 103143

Published: Nov. 1, 2024

Language: Английский

Citations

0