基于飞行状态数据的火箭动力系统异常监测研究

An Abnormal Detection Method of Rocket Power System Based on Improved Support Vector Machine

针对火箭动力系统内部传感器较少、可信度较低导致异常监测困难的问题,提出一种利用飞行状态信息进行火箭动力系统异常监测的方法.从火箭控制系统闭环回路中选取合适可测的飞行状态参数构建故障数据集;利用LSTM-自编码器算法提取故障特征信息;构建支持向量机分类辨识模型,并利用人工蜂群算法对于支持向量机的参数进行参数寻优,实现动力系统故障的异常监测;最后通过火箭飞行控制闭环回路仿真验证了所提算法的有效性和可行性.

Aiming at the problem of anomaly monitoring due to the insufficient sensors and low confidence level of the rocket power system,an improved support vector machine-based anomaly monitoring method for the rocket power system is proposed.Firstly,a closed-loop of the rocket control system is built,and the flight state dataset is constructed by selecting suitable measurable parameters.Secondly,the LSTM-Auto encoder algorithm is used to reconstruct the flight state data to obtain the residual data.Then,the support vector machine model is constructed,and the artificial bee colony algorithm is used to find the optimal classification parameters for the support vector machine parameters.The residual dataset is input to the support vector machine model.Finally,the effectiveness and feasibility of the algorithm are verified via the closed-loop simulation data.