A Multi-scale attention fusion framework based on KAN for rolling mill fault diagnosis

Key Points

• Improved fault diagnosis accuracy was achieved using a multi-scale approach, enhancing detection rates.
• The framework is based on KAN, integrating attention mechanisms to optimize diagnostic performance.
• Attention fusion combines multiple data scales, ensuring comprehensive analysis of faults in rolling mills.
• This method highlights the need for advanced machine learning techniques in industrial applications.