Early fault diagnosis based on reinforcement learning optimized-SVM model with vibration-monitored signals

Keyphrases

• Support Vector Machine Model 100%
• Reinforcement Learning 100%
• Improved Support Vector Machine 100%
• Early Fault Diagnosis 100%
• Early Fault 75%
• Fault Diagnosis 50%
• Vibration Signal 50%
• Rolling Bearing 50%
• Working Conditions 25%
• Time-frequency Domain 25%
• Economic Benefits 25%
• Experimental Validation 25%
• Diagnosis Approach 25%
• Support Vector Machine 25%
• Linear Discriminant Analysis 25%
• Machining Performance 25%
• Signal Base 25%
• Fault Characteristics 25%
• Public Dataset 25%
• Abnormal Vibration 25%
• Vibration Accelerometer 25%
• Corresponding Features 25%
• Multi-domain 25%
• Ambient Noise 25%
• Maintenance Performance 25%
• Fused Features 25%
• Hyperparameter Optimization 25%
• Machinery System 25%
• Status Assessment 25%
• Faulty Bearing 25%
• Challenging Problems 25%

Engineering

• Reinforcement Learning 100%
• Fault Diagnosis 100%
• Support Vector Machine 100%
• Rolling Bearings 40%
• Frequency Domain 40%
• Economic Benefit 20%
• Early Stage 20%
• Time Domain 20%

Chemical Engineering

• Reinforcement Learning 100%
• Support Vector Machine 100%