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Intelligent Fault Diagnosis of Rolling Mills Based on Gram Angular Difference Field and Dual-Attention Residual Network

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This method improves fault diagnosis accuracy in rolling mills using feature extraction and attention mechanisms.

The proposed method achieves superior fault diagnosis accuracy in rolling mills, with improved data feature extraction.
Average accuracy of at least 1.8% higher than existing methods on various imbalanced datasets was demonstrated.
The dual-attention mechanism enhances the re-integration of extracted features from converted GADF images.
Experimental validation confirms the effectiveness of the gramian angular difference field in monitoring rolling mills.

Hou et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

This method improves fault diagnosis accuracy in rolling mills using feature extraction and attention mechanisms.

The proposed method achieves superior fault diagnosis accuracy in rolling mills, with improved data feature extraction.
Average accuracy of at least 1.8% higher than existing methods on various imbalanced datasets was demonstrated.
The dual-attention mechanism enhances the re-integration of extracted features from converted GADF images.
Experimental validation confirms the effectiveness of the gramian angular difference field in monitoring rolling mills.

Hou et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark