Research on the condition monitoring method of unmanned aerial vehicle based on improved multivariate state estimation technique.

The widespread use of unmanned aerial vehicles (UAVs) stimulates the demand for condition monitoring methods. To this end, a wide range of condition monitoring methods have been developed to monitor UAVs' performance. However, since the relatively low accuracy of monitoring and high reliance on human experience for threshold setting, the performance of condition monitoring methods for UAVs is deficient. Therefore, this paper proposes an advanced condition monitoring method for UAVs which is composed of improved multivariate state estimation technique (IMSET) and a novel threshold setting method based on probability distribution. Firstly, the IMSET constructs memory matrix (MM) by dynamic selection with incremental learning to improve the accuracy of estimation. Secondly, the exponentially weighted moving average (EWMA) is employed to mitigate the impact of measurement errors in condition vectors and then the threshold is set by probability distribution to reduce the dependence on human experience. To verify the effectiveness of the proposed method, sufficient experiments based on condition monitoring data generated by DJI F450 are conducted. The experimental results demonstrate that the method proposed in this paper can accurately monitor the condition of UAVs in time.