Convolutional-Transformer Model with Long-Range Temporal Dependencies for Bearing Fault Diagnosis Using Vibration Signals

Keyphrases

• Vibration Signal 100%
• Bearing Fault Diagnosis 100%
• Transformer Model 100%
• Convolutional Transformer 100%
• Long-range Temporal Dependencies 100%
• Rotating Machinery 75%
• Fault Diagnosis 75%
• Training Data 25%
• Feature Extraction 25%
• Health Condition 25%
• Transformer 25%
• Temporal Relations 25%
• Deep Learning 25%
• Promising Solutions 25%
• Precision-recall 25%
• CNN Model 25%
• Bearing Fault 25%
• F1 Score 25%
• Dataset Size 25%
• Relationship Modeling 25%
• Distant Relationship 25%
• CNN Features 25%

Engineering

• Fault Diagnosis 100%
• Bearing Fault Diagnosis 100%
• Rotating Machinery 75%
• Convolutional Neural Network 50%
• Based Feature Extraction 25%
• Deep Learning Method 25%

Computer Science

• Fault Diagnosis 100%
• vibration signal 100%
• Convolutional Neural Network 33%
• Case Study 16%
• Feature Extraction 16%
• Valuable Source 16%
• Deep Learning Method 16%
• Training Dataset 16%
• Health Condition 16%
• Temporal Relationship 16%
• Relationship Modeling 16%