Physics-consistent constraint probabilistic modeling and prospective risk assessment method for intelligent...

Area of Science:

• Aerospace Engineering
• Control Systems
• Data Science

Background:

• Traditional methods for flight test monitoring face challenges with high dynamics, nonlinearity, and uncertainty.
• Existing models struggle to balance accuracy, physical consistency, and predictive warning capabilities.

Purpose of the Study:

• To propose a unified probabilistic framework for interpretable and calibratable evidence in safety-critical flight test decision-making.
• To enhance real-time monitoring by addressing limitations of traditional and data-driven approaches.

Main Methods:

• A closed-loop monitoring framework based on sequential Bayesian inference with three coupled modules: Physical Safety Assurance (PSA), Horizon Prediction and Risk (HPRF), and Evidence Generation and Uncertainty Quantification (EGUAQ).
• The PSA module integrates physical constraints and anomaly detection to prevent non-physical state jumps.
• The HPRF module provides multi-step predictions for risk assessment, balancing warning lead time and false alarm rates.
• The EGUAQ module dynamically assesses data value using posterior entropy for active verification.

Main Results:

• The proposed framework achieves high phase identification accuracy (Acc 0.969, F1 0.785) on simulated flight stall data.
• It provides an average warning lead time of approximately 3.34 seconds under controlled false alarm rates, outperforming baseline methods.
• Demonstrates excellent robustness and stability under sensor contradiction and out-of-distribution disturbances.

Conclusions:

• The developed framework establishes an interpretable analysis chain for situational awareness, risk quantification, and anomaly diagnosis in flight tests.
• It significantly enhances the engineering applicability and safety of flight test monitoring systems.