基于置信规则库的机载导弹故障诊断

针对机载导弹结构日益复杂,传统专家系统故障数据少、故障诊断效率低、准确率不高等问题,提出基于置信规则库的方法对机载导弹进行故障诊断。首先,描述了基于证据推理的置信规则库推理方法,建立输入与输出之间的非线性模型;其次,为解决传统专家系统中初始BRB参数不准确的问题,结合故障位置信息,建立参数优化学习模型;最后,以某型机载导弹的制冷系统为例,对基于置信规则库的机载导弹故障诊断方法进行了验证和对比。结果表明,该方法既能克服传统专家系统诊断效率低的问题,同时能够通过参数训练提高机载导弹的诊断精度,较好地提高了机载导弹故障诊断效率,为机载导弹的维护保障工作提供了参考。

Fault Diagnosis of Airborne Missile Based on Belief Rule Base

Aiming at the problem of increasingly complex structure of airborne missile, lack of diagnostic data in the traditional expert system, low efficiency and accuracy of fault diagnosis, a method based on Belief Rule Base (BRB) is proposed to diagnose the airborne missile. Firstly, this paper describes the reasoning method of BRB based on evidence reasoning and establishes the nonlinear model between input and output. Secondly, in order to solve the problem of inaccurate initial BRB parameters in traditional expert system, the parameter optimization learning model is established combining with the fault location information. Finally, taking a kind of airborne missile refrigeration system as an example, the fault diagnosis method of the airborne missile based on belief rule base is verified. The results show that this method can not only overcome the problem of low efficiency of traditional expert system diagnosis, but also improve the diagnosis accuracy of the airborne missile by parameter training. This method can improve the fault diagnosis efficiency of airborne missile and provide reference for the maintenance and support of airborne missile.