Compressive sensing strategy for classification of bearing faults

Engineering

• Compressive Sensing 100%
• Compressed Sensing 100%
• Rolling Element 100%
• vibration signal 100%
• Gaussians 50%
• Time Domain 50%
• Feature Extraction 50%
• Principal Components 50%
• Signal Processing 50%
• Component Analysis 50%
• Isolation Method 50%
• Classification Accuracy 50%
• Sensing Mechanisms 50%
• Sampling Rate 50%
• Classification Performance 50%
• Condition Monitoring 50%
• Compressed Data 50%
• Linear Feature 50%

Keyphrases

• Sensing Strategy 100%
• Compressed Sensing 100%
• Bearing Fault 100%
• Existing Techniques 50%
• Bearing Fault Diagnosis 50%
• Rolling Element Bearing 50%
• Principal Coordinate Analysis (PCoA) 25%
• Feature Extraction 25%
• Classification Performance 25%
• Signal Processing 25%
• Bearing Vibration Signal 25%
• Resampling 25%
• Bandwidth Utilization 25%
• Sampling Rate 25%
• Reduced Bandwidth 25%
• Feature Extraction Methods 25%
• Sensing Mechanism 25%
• Linear Discriminant Analysis 25%
• Vibration Signal 25%
• Machine Condition Monitoring 25%
• Rotating Machines 25%
• Large Storage 25%
• Compressed Data 25%
• Gaussian Random Matrix 25%
• Logistic Regression Classifier 25%
• Linear Feature Extraction 25%
• High Recognition Accuracy 25%