BearingFM: Towards a foundation model for bearing fault diagnosis by domain knowledge and contrastive learning

Computer Science

• Classification Accuracy 50%
• Computing Power 50%
• Contrastive Learning 100%
• Data Augmentation 50%
• Dimensional Space 50%
• Domain Knowledge 100%
• Essential Characteristic 100%
• Fault Diagnosis 100%
• Finished Product 50%
• Learning Framework 50%
• Massive Amount 50%
• Neural Network Model 50%
• Product Quality 50%
• Semisupervised Learning 50%
• Supply Chain 100%
• Time Dimension 50%
• vibration signal 50%

Engineering

• Classification Accuracy 50%
• Computing Power 50%
• Conducted Experiment 50%
• Dimensional Space 50%
• Domain Knowledge 100%
• Fault Diagnosis 100%
• Limitations 50%
• Network Model 50%
• Product Quality 50%
• Production Equipment 100%
• Quality Issue 50%
• Tasks 50%

Chemical Engineering

• Neural Network 100%
• Supervised Learning 100%