Physics-guided neural process with adaptive learning for uncertainty quantification of aero-engine remaining useful life

Key Points

• Aero-engine remaining useful life is effectively quantified using adaptive learning techniques, enhancing prediction reliability.
• The physical principles guide neural processes, increasing the model's predictive accuracy in uncertain conditions.
• This approach utilizes a physics-guided framework integrated with advanced neural networks for robust modeling.
• Findings may enable more effective maintenance strategies, enhancing operational reliability and safety.