Componential coding in the condition monitoring of electrical machines: Part 1: Principles and illustrations using simulated typical faults

Keyphrases

• Absolute Angular Position 50%
• Adaptive Neural Network 50%
• Anomaly Detection System 50%
• Arbitrary Displacements 50%
• Computationally Efficient 50%
• Condition Monitoring 100%
• Convolution Theorem 50%
• Data Processing Techniques 50%
• Data Signal 100%
• Electrical Machines 100%
• Fault Characteristics 50%
• Induction Motor 50%
• Machine Parts 100%
• Network Anomaly Detection 50%
• Numerical Examples 50%
• Periodic Convolution 100%
• Principal Coordinate Analysis (PCoA) 50%
• Rotating Parts 50%
• Tailored Technique 50%
• Time Invariance 50%
• Transverse Flux Motor 50%
• Typical Faults 100%
• Unseen 50%
• Unsupervised Data 50%
• Unsupervised Techniques 50%

Engineering

• Angular Position 50%
• Anomaly Detection 50%
• Component Analysis 50%
• Condition Monitoring 100%
• Convolution Theorem 50%
• Data Signal 100%
• Detection Technique 50%
• Electrical Machine 100%
• Illustrates 50%
• Induction Motor 50%
• Input Data 100%
• Machine Components 100%
• Numerical Example 50%
• Periodic Convolution 100%
• Principal Component 50%
• Processing Technique 50%
• Real Data 50%
• Reference Point 50%

Computer Science

• Adaptive Neural Network 50%
• Angular Position 50%
• Anomaly Detection 50%
• Component Analysis 50%
• Condition Monitoring 100%
• Convolution Theorem 50%
• Data Processing 50%
• Numerical Example 50%
• Periodic Convolution 100%
• Preprocessing 50%
• Principal Component 50%
• Reference Point 50%
• Unsupervised Data 50%
• Unsupervised Technique 50%