Enhanced Fault Detection in Satellite Attitude Control Systems Using LSTM-Based Deep Learning and Redundant Reaction Wheels
Reliable fault detection in satellite attitude control systems stands as a critical aspect of ensuring the safety and success of space missions. Central to these systems, reaction wheels (RWs), despite being the most frequently used actuators, present a vulnerability given their susceptibility to fa...
Saved in:
| Main Author: | Sajad Saraygord Afshari |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/12/856 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Enhanced Fault Diagnosis in Rotating Machinery Using a Hybrid CWT-LeNet-5-LSTM Model: Performance Across Various Load Conditions
by: Muhammad Ahsan, et al.
Published: (2025-01-01) -
FAULT DIAGNOSIS OF WIND TURBINE BEARING BASED ON SENET-RESNEXT-LSTM
by: DU HaoFei, et al.
Published: (2023-12-01) -
Aircraft Bearing Fault Diagnosis Method Based on LSTM-IDRSN
by: Lei Wang, et al.
Published: (2025-01-01) -
Development of Robot Feature for Stunting Analysis Using Long-Short Term Memory (LSTM) Algorithm
by: Muhammad Rahadian Abdurrahman, et al.
Published: (2024-10-01) -
Daily flow discharge prediction using integrated methodology based on LSTM models: Case study in Brahmani-Baitarani basin
by: Abinash Sahoo, et al.
Published: (2024-01-01)