Automated Design of Fault Diagnosis CNN Network for Satellite Attitude Control Systems
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View abstract on PubMed
Summary
This summary is machine-generated.Supervised anomaly detection using a novel CNN architecture improves satellite attitude control system fault diagnosis. The MPSO-ACBCNN method offers higher accuracy and efficiency for critical satellite components.
Area Of Science
- Spacecraft Engineering
- Artificial Intelligence
- Fault Diagnosis
Background
- Unsupervised and self-supervised methods dominate satellite fault diagnosis.
- Supervised methods offer advantages in sensitivity and deployment for subsystems like ACSs.
- Existing CNN designs face challenges in accuracy and over-design for satellite ACS fault diagnosis.
Purpose Of The Study
- To introduce a novel CNN architecture for satellite attitude control system (ACS) fault diagnosis.
- To address over-design and accuracy issues in current CNN-based fault diagnosis methods.
- To develop an optimized CNN model for high-sensitivity and lightweight fault detection.
Main Methods
- Developed the Advanced Convolution Blocks-based CNN (ACBCNN) with efficient design elements and dense connections.
- Introduced the Modified Particle Swarm Optimization-ACBCNN (MPSO-ACBCNN) algorithm for optimizing the ACBCNN architecture.
- Implemented fitness design balancing parameter quantity and training effectiveness, and methods for feasible solutions within MPSO.
Main Results
- The ACBCNN architecture enhances feature extraction from power spectral density (PSD) graphs.
- The MPSO-ACBCNN algorithm effectively optimizes the CNN for ACS fault diagnosis.
- Numerical experiments confirmed the effectiveness and superiority of the MPSO-ACBCNN method.
Conclusions
- The MPSO-ACBCNN method provides a superior approach for satellite ACS fault diagnosis.
- This method achieves high-sensitivity detection and lightweight deployment for critical subsystems.
- The proposed approach overcomes limitations of existing CNN designs in satellite fault diagnosis.
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