摘要
针对基于专家知识的故障诊断方法依赖经验的局限,提出一种基于层次分析法(AHP)的贝叶斯网络化工过程故障诊断方法。通过基于关联函数的AHP得到所有变量的权值,对22个变量节点的权值进行排序并将该排序作为K2算法的学习输入建立贝叶斯网络模型,同时结合复杂网络分析指标进行化工过程的故障诊断。通过TE过程故障诊断实例证明本文方法不仅避免了K2算法专家知识的主观因素影响,同时能很好地进行故障定位,找到故障源。
A chemical process fault diagnosis method based on the analytic hierarchy process （AHP） is proposed in order to overcome the limitations of experience knowledge based on expert knowledge. The weight of all the varia- bles is obtained by AHP based on the correlation function. The weight of the 22 variable nodes is sorted and the or- der is used as the learning input of the K2 algorithm to establish the Bayesian network model. At the same time, the chemical process is combined with the complex network analysis index Troubleshooting. The fault diagnosis ex- ample of the TE process shows that this method not only avoids the influence of subjective factors in K2 algorithm expert knowledge, but also can locate fault location accurately and find the fault source.
作者
耿志强
张玉婷
韩永明
GENG ZhiQiang ZHANG YuTing HAN YongMing(College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China)
出处
《北京化工大学学报（自然科学版）》
CAS
CSCD
北大核心
2017年第5期99-104,共6页
Journal of Beijing University of Chemical Technology(Natural Science Edition)
基金
国家自然科学基金(61374166/61603025)
北京市自然科学基金(4162045)
关键词
贝叶斯网络
层次分析法
K2算法
故障诊断
TE过程
Bayesian network
analytic hierarchy process
K2 algorithm
fault diagnosis
TE process
