摘要
为解决DC-DC电源模块故障诊断中不确定性的相关问题,研究了电源模块故障不确定性产生的原因,同时对不确定性信息类型与推理方法进行了研究。利用贝叶斯网络对DC-DC电源模块故障产生原因与故障模式进行建模描述,经过BIC与K2评分算法训练完成后,可以进行不确定性推断。在故障概率方面分析了模块中的不同故障部件对电源模块故障的影响,利用MATLAB平台根据模拟数据对不确定性模型进行了分析,验证了基于贝叶斯网络的故障预测不确定性模型的有效性。
In order to solve the uncertainty related problems in DC-DC power module fault diagnosis, the causes of power module fault uncertainty are studied. At the same time, the uncertainty information type and reasoning method are studied. The Bayesian network is used to describe the cause and failure mode of the DC-DC power module fault. After the BIC and K2 scoring algorithms are completed, the uncertainty can be inferred. The influence of different fault components in the module on the fault of the power module is analyzed in terms of the probability of failure. The MATLAB platform is used to analyze the uncertainty model based on the simulation data, and the effectiveness of the Bayesian network-based fault prediction uncertainty model is verified.
作者
贾晓晓
高会壮
黄姣英
JIA Xiaoxiao;GAO Huizhuang;HUANG Jiaoying(Military Delegate Office of PLA Rocket Force at Fact.699, Beijing 100039, China;School of Reliability and Systems Engineering, Beihang University, Beijing 100191, China)
出处
《电子与封装》
2019年第4期28-31,44,共5页
Electronics & Packaging
