A Deep Learning Model for Remaining Useful Life Prediction of Aircraft Turbofan Engine on C-MAPSS Dataset

Keyphrases

• Remaining Life Prediction 100%
• Deep Learning Model 100%
• Aircraft Turbofan Engine 100%
• C-MAPSS Dataset 100%
• Remaining Useful Life 66%
• Long Short-term Memory Network 50%
• Piecewise Linear 33%
• C-MAPSS 33%
• Engine Degradation 33%
• Dimensionality Reduction 16%
• Performance Improvement 16%
• Prediction Accuracy 16%
• Correlation Analysis 16%
• Downtime 16%
• Value Function 16%
• Preprocessing Techniques 16%
• Sensor Data 16%
• Root Mean Square Error 16%
• Industry 4.0 (I4.0) 16%
• Sensor Measurement 16%
• Degradation Model 16%
• Maintenance Operations 16%
• Deep Neural Network 16%
• Median Filter 16%
• Industrial Machinery 16%
• Deep Learning Methods 16%
• Safety Efficiency 16%
• Safety Reliability 16%
• Life Functioning 16%
• Deep Long Short-term Memory Network 16%
• Due Time 16%
• Linear Degradation Model 16%
• Pre-processed Data 16%
• Squared Error Function 16%
• Prognostic Methods 16%
• Industry Safety 16%
• Sensor Datasets 16%
• Algorithm of Functioning 16%
• Target Labels 16%
• Turbofan Engine 16%
• Score Function 16%
• Moving Median 16%
• Trade Sector 16%
• Yield Improvement 16%

Engineering

• Life Prediction 100%
• Deep Learning Method 100%
• Remaining Useful Life 100%
• Turbofan Engine 100%
• Long Short-Term Memory Network 40%
• Starting Point 20%
• Degradation Model 20%
• Dimensionality 10%
• Experimental Work 10%
• Past Decade 10%
• Sensor Measurement 10%
• Sensor Data 10%
• NASA 10%
• Processing Step 10%
• Industry 4.0 10%
• Deep Neural Network 10%
• Operation and Maintenance 10%
• Industrial Machinery 10%
• Error Function 10%
• Root-Mean-Squared Error 10%
• Processed Data 10%
• Median Filter 10%
• Score Function 10%