BP神经网络辅助的缓变故障双阈值检测法

A double-threshold test method for soft faults assisted by BP neural network

针对传统χ~2检测法对惯性/卫星组合导航缓变故障检测效率不高的问题,提出一种基于BP神经网络辅助的缓变故障双阈值检测法.基于BP神经网络建立位置与速度子预测器,实现对卫星导航量测数据的预测,在此基础上根据预测精度提出双阈值的低检测门限,辅助残差χ~2检测法进行故障检测与系统重构.仿真结果表明,对于缓变故障,所提出方法能有效提高故障期间滤波精度、降低漏警率以及组合导航的可靠性.

Aiming at the problem that the low efficiency of detecting the soft faults by the traditional chi-square test method in the fault diagnosis of loosely-coupled inertial/satellite integrated navigation systems, a double-threshold test method to detect soft faults assisted by BP neural network is proposed. The location and velocity sub-predator is established by BP neural network, which can realize the prediction of satellite measurement data. On this basis, a double-threshold test is proposed according to the prediction accuracy, which can assist the residual chi-square component detection method to detect faults and reconstruct the system. The simulation results show that the proposed method can effectively improve the filtering accuracy when fault occurs, and reduce the missed alarm rate and improve the reliability of integrated navigation.