SHM-informed life-cycle intelligent maintenance of fatigue-sensitive detail using Bayesian forecasting and Markov decision process

Keyphrases

• Markov Decision Process 100%
• Bayesian Forecasting 100%
• Intelligent Maintenance 100%
• Partially Observable Markov Decision Process 100%
• Optimal Maintenance 50%
• Engineering Systems 25%
• Structural Health Monitoring 25%
• Civil Engineering 25%
• Structural Performance 25%
• Corrosion 25%
• Performance Metrics 25%
• Monitoring Data 25%
• Deterioration Rate 25%
• Maintenance Activity 25%
• Management Framework 25%
• Service Life 25%
• Structural Conditions 25%
• Fatigue Process 25%
• Failure Risk 25%
• Long-term Monitoring Data 25%
• Action-based 25%
• Optimal Management 25%
• Condition Index 25%
• Continuous Observation 25%
• Life Cycle Management 25%
• Stochastic Environment 25%
• State Transition Matrix 25%
• Welded Details 25%
• Dynamic Strain Response 25%
• Chronic Fatigue Syndrome 25%
• Long-term Dynamics 25%
• Sensitive Characteristics 25%
• Real-world Monitoring 25%
• Bayesian Dynamic Linear Model 25%
• Multiple Interventions 25%
• Integrated Monitoring 25%
• Structural Interventions 25%
• Maritime Engineering 25%
• Adaptive Discretization 25%
• Operational Life 25%
• Long-term Utility 25%

Engineering

• Lifecycle 100%
• Markov Decision Process 100%
• Partially Observable Markov Decision Process 100%
• Monitoring Data 50%
• Structural Health Monitoring 25%
• Applicability 25%
• Acceptable Level 25%
• Maintenance Strategy 25%
• Strain Response 25%
• Cycle Management 25%
• Maintenance Action 25%
• Naval Architecture 25%
• Operational Life 25%
• State Transition Matrix 25%
• Metrics 25%
• Civil Engineering 25%
• Discretization 25%
• Service Life 25%