针对传统故障诊断方法检测某型潜舰导弹武器系统故障准确率不高、耗时长的问题，提出基于概率神经网络的智能诊断方法。介绍该网络的典型结构及优势所在，以某型潜舰导弹武器系统为验证对象，选取合适特征向量、归纳合理故障类型、建立相应神经网络，并运用Matlab仿真验证。结果表明在现有数据库中，概率神经网络对该系统的故障诊断正确率为77.8%。这表明基于概率神经网络的故障诊断基本能够区分该系统故障类型，大大减少了部队故障诊断时间和人力投入。
Aiming at the problems of low accuracy and long time for traditional fault diagnosis methods to detect the fault of a submarine missile weapon system,an intelligent diagnosis method based on probabilistic neural network was proposed.This paper introduces the typical structure and advantages of the network,takes a certain submarine missile weapon system as the verification object,selects the appropriate feature vector,summarizes the reasonable fault types,establishes the corresponding neural network,and uses Matlab simulation to verify.The results show that:in the existing database,the probabilistic neural network fault diagnosis accuracy of the system is 77.8%.This shows that the fault diagnosis based on probabilistic neural network can effectively distinguish the fault types of the system,and greatly reduce time and manpower input of the army fault diagnosis.
2024,46(16): 182-185 收稿日期：2023-10-20
DOI：10.3404/j.issn.1672-7649.2024.16.032
分类号：TJ09
作者简介：冯林平（1975 – ），男，硕士，副教授，研究方向为战术导弹
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