Fault Diagnosis Technique for the Reel Bearing of a Cigarette Manufacturing Machine utilising Multi-Level Feature Fusion

Key Points

• The aim is to develop a fault diagnosis technique for the reel bearing of cigarette manufacturing machines using multi-level feature fusion.
• Utilized advanced signal processing techniques to extract features from machine data.
• Integrated multiple feature sets to enhance diagnostic accuracy.
• Applied machine learning algorithms for fault classification.
• Achieved improved fault detection accuracy compared to traditional methods.
• Enabled early identification of potential failures in reel bearings.