Sequential Feature-Augmented Deep Multilabel Learning for Compound Fault Diagnosis of Rotating Machinery with Few Labeled and Imbalanced Data

Keyphrases

• Imbalanced Data 100%
• Multi-label Learning 100%
• Limited Labeled Data 100%
• Rotating Machinery Fault Diagnosis 100%
• Compound Fault Diagnosis 100%
• Sequential Features 100%
• Learning Model 50%
• Compound Fault 50%
• Mechanical Systems 25%
• Data-driven Methods 25%
• Safe Operation 25%
• Diagnostic Methods 25%
• Single Fault 25%
• Rotating Machinery 25%
• Convolutional Neural Network 25%
• Sensing Data 25%
• Stacked Autoencoder 25%
• Large-scale Sensing 25%
• Time Sequential 25%
• Convolutional Long Short-term Memory (ConvLSTM) 25%
• Smoothing Method 25%
• Frequent Occurrence 25%

Computer Science

• Imbalanced Data 100%
• Fault Diagnosis 100%
• Convolutional Neural Network 25%
• Long Short-Term Memory Network 25%
• Sequential Time 25%
• Experimental Result 25%
• Sparse Autoencoder 25%