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.

中文翻译：

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基于多传感器信息融合和模型关联的跑道摩擦系数估计

摩擦是影响着陆或起飞时发生航空事故的关键因素。轮胎与跑道的摩擦直接有助于飞机在陆地上的稳定性。因此，对所有利益相关者来说，准确的摩擦估算是一个日益严重的问题。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，胎面传感器和其他物理传感器形成一个传感器系统，用于测量与摩擦有关的参数并通过神经网络将其融合。迄今为止，已经进行了许多尝试来将地面摩擦系数与飞机制动摩擦系数联系起来。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）和其他拟合模型。此外，本文尝试将神经网络的输出（估计的摩擦系数）与相关模型相关联，以预测飞机制动时轮胎与跑道之间的摩擦系数。本文提出的传感器系统可以看作是一种移动天气-跑道-轮胎系统，它可以通过综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作用。相关模型的作用是将地面摩擦系数转换为飞机制动摩擦系数的等级，并最终将信息报告给飞行员，以便他们做出更好的决策。本文试图将神经网络的输出（估计的摩擦系数）与相关模型进行关联，以预测飞机制动时轮胎与跑道之间的摩擦系数。本文提出的传感器系统可以看作是一种移动天气-跑道-轮胎系统，它可以通过综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作用。相关模型的作用是将地面摩擦系数转换为飞机制动摩擦系数的等级，并最终将信息报告给飞行员，以便他们做出更好的决策。本文试图将神经网络的输出（估计的摩擦系数）与相关模型进行关联，以预测飞机制动时轮胎与跑道之间的摩擦系数。本文提出的传感器系统可以看作是一种移动天气-跑道-轮胎系统，它可以通过综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作用。相关模型的作用是将地面摩擦系数转换为飞机制动摩擦系数的等级，并最终将信息报告给飞行员，以便他们做出更好的决策。