基于BP神经网络的某防空导弹发射机构故障分析

在武器装备维修与技术保障领域中，普遍存在故障模式复杂、分析定位繁琐等问题，一定程度上影响了装备维修工作的效率。为解决上述问题，以陆军某型便携防空导弹发射机构为典型研究对象，设计了一种基于人工神经网络的故障分析方法，研究了神经网络在装备技术保障领域中的应用。该方法以BP神经网络为基础，利用历史维修数据确定网络的正反向传播矩阵，在后期采取一定措施对预测准确率进行了优化，整套方法利用Python环境进行了实现。通过实验验证，该方法对发射机构故障进行定位时，速度快、效率高，准确率超过96%，因此可知该方法对于多种故障现象影响下的故障模式分析具有较高的预测准确率，能满足装备维修中故障模式的快速分析定位，并且具有较强的通用性。

Fault Analysis of Launching Mechanism of an Air Defense Missile Based on BP Neural Network

In the field of weapon equipment maintenance and technical support, there are many problems, such as complex failure modes and complicated analysis and location, which affect the efficiency of equipment maintenance to a certain extent. In order to solve the above problems, a fault analysis method based on artificial neural network is designed on the typical platform of a portable air defense missile launching mechanism of the Army. The application of neural network in the field of equipment technical support is studied. This method is based on BP neural network and uses historical maintenance data to determine the forward and backward propagation matrix of the network. In the later stage, some measures are taken to optimize the classification accuracy. The whole method is implemented in Python environment. Experiments show that this method has high speed, high efficiency and accuracy over 96% when locating the fault of launching mechanism. Therefore, this method has high classification accuracy for fault mode analysis under the influence of various fault phenomena, and can satisfy the rapid analysis and location of fault mode in equipment maintenance, and has strong versatility.