基于贝叶斯网络的DC-DC电源故障不确定性分析
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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.
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.
引文
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[2]刘京津.基于多智能体系统的故障诊断技术在智能电网中的应用[J].电子与封装,2013,13(12):43-48.
[3]郭日红,董忠文,谢国锋.基于贝叶斯网络的机械设备故障诊断方法研究[J].机械设计与制造工程,2016,45(10):87-91.
[4]陈淑鑫,李敬有,张凌宇.模糊贝叶斯网络融合多传感器故障信息可靠性方法[J].现代电子技术,2016,39(14):15-19.
[5]郭文强,彭程,张宝嵘,等.基于分布式贝叶斯网络的多故障诊断方法研究[J].现代电子技术,2017,40(1):119-124.
[6]冯庚,蒋雨宏,范路,等.基于事故树分析与贝叶斯网络的土石坝风险分析[J].水力发电,2013,39(4):34-36.
[7]姚成玉,陈东宁,王斌.基于TS故障树和贝叶斯网络的模糊可靠性评估方法[J].机械工程学报,2014,50(2):193-201.
[8]Wu C,Jiang F,Wang Q,et al.Bayesian network methods for fault diagnosis of power systems[C].Electrical and Control Engineering(ICECE),2011 International Conference on,IEEE,2011:1012-1015.
[9]Ghotra B,McIntosh S,Hassan A E.Revisiting the impact of classification techniques on the performance of defect prediction models[C].Proceedings of the 37thInternational Conference on Software Engineering-Volume 1.IEEE Press,2015:789-800.