Fault logic and data-driven model for operation reliability analysis of the flap deflection angle

Area of Science:

• Aerospace Engineering
• Mechanical Reliability
• Computational Intelligence

Background:

• Flap deflection angle reliability analysis is crucial for aircraft safety.
• Existing methods struggle with uncertain fault parameters.
• Physics-informed machine learning offers new avenues for structural integrity.

Purpose of the Study:

• To develop a novel reliability analysis framework for flap deflection angles.
• To integrate fault logic and data-driven modeling for enhanced accuracy.
• To introduce an optimized algorithm for improved reliability modeling.

Main Methods:

• Fault logic analysis to identify characteristic fault parameters.
• Data-driven modeling for reliability analysis with limited data.
• Improved dung beetle optimization algorithm for back propagation (IDBO-BP) for flap deflection modeling.

Main Results:

• The proposed framework accurately reflects actual flap motion.
• The IDBO-BP algorithm demonstrates superior modeling and simulation capabilities.
• Effective reliability analysis is achieved even with uncertain fault parameters.

Conclusions:

• The developed framework provides a robust solution for flap deflection reliability analysis.
• The IDBO-BP algorithm offers a significant advancement in optimization for reliability modeling.
• This study contributes a new approach to structural integrity applications using machine learning.