基于多源信息融合的火炮装填状态监测与故障诊断系统

针对火炮装填系统故障成因复杂、诊断方法不足等问题，提出一种基于多源信息融合的火炮装填监测与 故障诊断的方法。采用知识决策属性进行属性分类，构建神经网络训练模型，对自动装填系统故障进行定性分析并 建立故障诊断辨识框架，根据故障决策准则得出故障判断，解决了故障特征的不确定性、故障模式多样性的问题。 实例分析结果表明，该方法达到了有效提高故障诊断确诊率的目的。

Artillery Filling State Monitoring and Fault Diagnosis System Based on Multi-source Information Fusion

Aiming at the problems of complex failure of the artillery filling system and the lack of diagnostic methods, a method of gun loading monitoring and fault diagnosis based on multi-source information fusion is proposed. The knowledge decision attribute is used to classify the attributes, and the neural network training model is constructed. The faults of the automatic loading system are qualitatively analyzed and the fault diagnosis identification framework is established. The fault judgment is obtained according to the fault decision criteria, and the uncertainty of the fault features and the varied fault modes are solved. The example analysis shows that the method achieves the goal of effectively improving the diagnosis rate of fault diagnosis.