多元数据融合在无人机结构-健康监测中的应用

Abstract

Structural health monitoring is an important means to guarantee the continuing safe operation of aircrafts, and makes a key technique for unmanned aerial vehicles’ (UAVs’) development and certification. For a UAV fuselage, the structural acceleration responses, strain signals and modal parameters were acquired on-line from different sensor measurements in dynamic structure simulation. The normalized wavelet packet energy change rate index, the strain energy change rate index, the modal frequency change rate index and the mixed damage evaluation indices were built to indicate the structural health condition. The integrated multi-source data fusion technique, including data-level fusion, feature-level fusion and Bayesian probabilistic neural network-based decision-level fusion, was used with the rough set reduction successively to significantly decrease the spatial dimension of data. The mapping between structural damage information, like damage severity, damage locations and health evaluation indices, was established, and the comprehensive decision of the structural damage model was achieved. An example for the health monitoring of an unmanned helicopter was demonstrated. The experimental results verify the accuracy of the proposed data fusion technique for damage identification of multi-damage aircraft structures, and show the validity of multi-data fusion in UAV health monitoring.