为监测无人机舵面系统的工作状态，提出一种集合经验模态分解和后验概率下多分类支持向量机相结合的诊断方法。该方法将飞机方向舵的正常状态、松浮状态、损伤状态、卡死状态以及反向状态等5种典型工况下的输出信号作为研究对象，首先将采集到的信号进行集合模态经验分解，得到一系列成分简单的固有模态函数，然后分别计算各阶分量的能量值并以此构成信号特征矢量，最后以此作为输入信息建立基于后验概率的多分类支持向量机，进而判定飞机舵面系统的故障类型。仿真实验结果表明，该方法可以有效地应用于舵面系统的故障诊断。

To monitor the working conditions of UAV's actuator system in time, a novel fault-diagnosis approach was proposed based on Ensemble Empirical Mode Decomposition (EEMD) and the multi-class Support Vector Machine (SVM) with posterior probability. The actuator signals obtained under five typical working conditions were taken as the object of study, i.e., normal condition, loose condition, damaged condition, stuck condition and reverse condition.First, EEMD was made to the collected signals, which were decomposed into a series of Intrinsic Mode Functions (IMFs) with simple components. Then, the energy values of the components of each order were calculated, by which the signal feature vector was obtained. Finally, the multi-class SVM based on posterior probability was established according to the feature information, and thus the type of the aircraft actuator system fault was identified.Simulation results show that the proposed approach can be applied to the fault diagnosis of the actuator system.