A novel method based on UNET for bearing fault diagnosis

Keyphrases

• Vibration Image 100%
• Bearing Fault Diagnosis 100%
• Rotating Machines 75%
• Deep Learning Model 50%
• Fault Diagnosis 50%
• Condition Monitoring 50%
• Comparative Analysis 25%
• Working Conditions 25%
• Deep Learning 25%
• F1 Score 25%
• Feature Learning 25%
• Fault Condition 25%
• Sliding Window 25%
• Labeling Method 25%
• Dense Prediction 25%
• Reliable Operation 25%
• Pixel-level Feature 25%
• Vibration Data 25%
• Monitoring Approach 25%
• Label Information 25%
• Recent Past 25%
• Normalized Amplitude 25%

Engineering

• Deep Learning 100%
• Fault Diagnosis 100%
• Condition Monitoring 66%
• Comparative Analysis 33%
• Fault Condition 33%
• Reliable Operation 33%
• Pixel Level 33%
• Data Sample 33%
• Level Feature 33%
• Obtained Image 33%

Chemical Engineering

• Deep Learning 100%
• Condition Monitoring 66%