This paper presents a preflight diagnosis method for detecting multicopter’s motor abnormalities using jig equipment data. While operating multicopters on a regular basis, determining whether it can perform the flight or not is important. For this, we use disturbance observer’s output as a feature for detecting degree of the abnormality by Gaussian process regression. During the ground inspection test where most of the disturbances are under control, motor degradation and disturbances are significantly correlated. Then, motor degradation can be estimated using the Gaussian process regression. To create multivariate output models against different degrees of motor abnormalities, we use multitask a Gaussian process regression model. To verify the performance of the proposed approach, actual preflight tests on a ground jig device developed in-house were performed with an actual quadcopter drone.

本文提出了一种使用夹具设备数据检测多直升机电动机异常的飞行前诊断方法。在定期操作多旋翼飞机时，确定其是否可以执行飞行很重要。为此，我们将干扰观测器的输出作为通过高斯过程回归检测异常程度的功能。在大部分干扰都得到控制的地面检查测试中，电动机的退化和干扰之间存在显着的相关性。然后，可以使用高斯过程回归来估计运动退化。为了创建针对不同程度的电机异常的多元输出模型，我们使用多任务高斯过程回归模型。为了验证所建​​议方法的性能，