Estimation for Runway Friction Coefficient Based on Multi-Sensor Information Fusion and Model Correlation

Area of Science:

• Aerospace Engineering
• Materials Science
• Sensor Technology

Background:

• Tire-runway friction significantly impacts aircraft safety during landing and takeoff.
• Accurate friction estimation is critical for preventing accidents and ensuring aircraft stability.
• Existing friction measurement methods have limitations.

Purpose of the Study:

• To propose a novel multi-sensor information fusion scheme for estimating tire-runway friction coefficients.
• To correlate estimated ground friction with aircraft braking friction for practical application.
• To enhance pilot decision-making through real-time friction data.

Main Methods:

• A sensor system integrating acoustic, optical, and tread sensors to measure friction-related parameters.
• A neural network for fusing multi-sensor data to estimate the friction coefficient.
• Correlation modeling to link estimated ground friction with aircraft braking friction.

Main Results:

• A multi-sensor system effectively estimates tire-runway friction coefficients.
• The proposed system integrates runway surface and tire conditions for comprehensive analysis.
• Correlation models successfully predict aircraft braking friction from estimated ground friction.

Conclusions:

• The developed sensor system and fusion scheme provide a robust method for friction estimation.
• This approach offers a mobile weather-runway-tire system for real-time friction assessment.
• The findings contribute to improved aviation safety by enabling better pilot decision-making.