A co-training-based approach for prediction of remaining useful life utilizing both failure and suspension data

Keyphrases

• Suspension Data 100%
• Failure Data 100%
• Training Base 100%
• Remaining Life Prediction 100%
• Co-training 100%
• Engineered Systems 50%
• Individual Algorithms 50%
• System Lifetime 33%
• Data-driven Prognostics 33%
• Condition Monitoring Data 33%
• Suspension Units 33%
• Good Accuracy 16%
• Training Data 16%
• System Failure 16%
• Traditional Data 16%
• Online Prediction 16%
• Out of Service 16%
• Degradation Trend 16%
• Online Training 16%
• Prognostic Approach 16%
• Remaining Useful Life 16%
• Data-driven Algorithm 16%
• Prognostic Accuracy 16%
• Virtual Failure 16%

Engineering

• Failure Data 100%
• Monitoring Data 66%
• Condition Monitoring 66%
• Life Prediction 66%
• Suspension Unit 66%
• Good Accuracy 33%
• Offline Training 33%

Chemical Engineering

• Condition Monitoring 100%