A data-driven prognostics framework for tool remaining useful life estimation in tool condition monitoring

Computer Science

• Condition Monitoring 100%
• Feature Selection 66%
• Feature Extraction 66%
• Machine Learning Algorithm 33%
• Production Efficiency 33%
• Layer Neural Network 33%
• Simulation Performance 33%
• Data Preprocessing 33%
• Driven Framework 33%
• Data Alignment 33%
• Experimental Dataset 33%
• Point Detection 33%
• Multiple Sensor 33%
• Product Quality 33%
• Good Performance 33%
• Sensor Measurement 33%
• Model Selection 33%

Engineering

• Feature Extraction 100%
• Condition Monitoring 100%
• Remaining Useful Life 100%
• Multiple Sensor 33%
• Selection Method 33%
• Sensor Measurement 33%
• Product Quality 33%
• Good Performance 33%
• Process Window 33%
• Thrust Force 33%
• Machine Learning Algorithm 33%
• Data Preprocessing 33%
• vibration signal 33%

Keyphrases

• Tool Remaining Useful Life 100%