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

Sensors
Yadong NiuWei Zhou

Abstract

Friction is a crucial factor affecting air accident occurrence on landing or taking off. Tire-runway friction directly contributes to aircraft stability on land. Therefore, an accurate friction estimation is a rising issue for all stakeholders. This paper summarizes the existing measurement methods, and a multi-sensor information fusion scheme is proposed to estimate the friction coefficient between the tire and the runway. Acoustic sensors, optical sensors, tread sensors, and other physical sensors form a sensor system that is used to measure friction-related parameters and fuse them through a neural network. So far, many attempts have been made to link the ground friction coefficient with the aircraft braking friction coefficient. The models that have been developed include the International Runway Friction Index (IRFI), Canada Runway Friction Index (CRFI), and other fitting models. Additionally, this paper attempts to correlate the output of the neural network (estimated friction coefficient) with the correlation model to predict the friction coefficient between the tire and the runway when the aircraft brakes. The sensor system proposed in this paper can be regarded as a mobile weather-runway-tire system, which can estimate...Continue Reading

Software Mentioned

TALPA
IVSS
NAVBLUE
JWRFMP

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