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基于多传感器信息融合与模型关联的跑道摩擦系数估计

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

作者信息

Niu Yadong, Zhang Sixiang, Tian Guangjun, Zhu Huabo, Zhou Wei

机构信息

School of Mechanical Engineering, Hebei University of Technology, Tianjin 300130, China.

出版信息

Sensors (Basel). 2020 Jul 13;20(14):3886. doi: 10.3390/s20143886.

DOI:10.3390/s20143886
PMID:32668618
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7412354/
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 the friction coefficient by integrating the runway surface conditions and the tire conditions, and fully consider their common effects. The role of the correlation model is to convert the ground friction coefficient to the grade of the aircraft braking friction coefficient and the information is finally reported to the pilots so that they can make better decisions.

摘要

摩擦力是影响飞机起降事故发生的关键因素。轮胎与跑道间的摩擦力直接关系到飞机在地面上的稳定性。因此，精确的摩擦力估计对于所有利益相关方而言都是一个日益重要的问题。本文总结了现有的测量方法，并提出了一种多传感器信息融合方案来估计轮胎与跑道之间的摩擦系数。声学传感器、光学传感器、胎面传感器及其他物理传感器构成一个传感器系统，用于测量与摩擦相关的参数，并通过神经网络对其进行融合。到目前为止，已经进行了许多尝试来将地面摩擦系数与飞机刹车摩擦系数联系起来。已开发的模型包括国际跑道摩擦指数（IRFI）、加拿大跑道摩擦指数（CRFI）以及其他拟合模型。此外，本文尝试将神经网络的输出（估计的摩擦系数）与相关模型进行关联，以预测飞机刹车时轮胎与跑道之间的摩擦系数。本文提出的传感器系统可被视为一个移动的天气 - 跑道 - 轮胎系统，它能够通过整合跑道表面状况和轮胎状况来估计摩擦系数，并充分考虑它们的共同影响。相关模型的作用是将地面摩擦系数转换为飞机刹车摩擦系数等级，最终将信息报告给飞行员，以便他们做出更好的决策。

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本文引用的文献

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Multi-sensor Fusion Road Friction Coefficient Estimation During Steering with Lyapunov Method.基于李雅普诺夫方法的转向过程中多传感器融合道路摩擦系数估计
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2
Three Three-Axis IEPE Accelerometers on the Inner Liner of a Tire for Finding the Tire-Road Friction Potential Indicators.安装在轮胎内衬层上的三个三轴IEPE加速度计，用于寻找轮胎-路面摩擦潜在指标。
Sensors (Basel). 2015 Aug 5;15(8):19251-63. doi: 10.3390/s150819251.
Sensors (Basel). 2021 Nov 16;21(22):7621. doi: 10.3390/s21227621.