基于小波包分析和Elman神经网络的军用电源智能故障诊断

军用电源作为地空导弹系统的供电设备，其可靠运转关系到地空导弹系统作战效能的发挥。为实现地空导弹电源逆变器的故障诊断与容错运行，将小波包分解与Elman神经网络结合进行故障特征提取及故障辨识，并应用于地空导弹静变电源的故障诊断。在准确诊断出故障的基础上，利用故障隔离切换电路，隔离故障桥臂，投入备用桥臂，保证静变电源继续正常运行。故障诊断和故障重构仿真的效果验证了该方法的有效性。

Fault diagnosis of military power based on wavelet packet analysis and elman neural network

The reliable operation of power supply is very important to the operational effectiveness of surface-to-air missile weapon system. In order to realize fault diagnosis and fault tolerant operation of surface-to-air missile power supply inverter, the wavelet packet analysis and Elman neural network were introduced. Wavelet packet analysis combined with Elman neural network were applied to the power failure diagnosis of missile power supply system for the fault feature extraction and fault identification. On the basis of accurate fault diagnosis, fault isolation circuit is used to isolate fault components. The backup bridge arm is put into use to replace fault components so that the system can continue to operate normally. The results of the fault diagnosis and fault reconfiguration simulation verify the effectiveness of the method.