典型文献
A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant
文献摘要：
With the increasing intelligence and integration,a great number of two-valued variables(generally stored in the form of 0 or 1)often exist in large-scale industrial processes.However,these variables cannot be effectively handled by traditional monitoring methods such as linear discriminant analysis(LDA),principal component analysis(PCA)and partial least square(PLS)analysis.Recently,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality monitoring.Although the MHNBM is effective,it still has some shortcomings that need to be improved.For the MHNBM,the variables with greater correlation to other variables have greater weights,which can not guarantee greater weights are assigned to the more discriminating variables.In addition,the conditional probability P(xj|xj',y=k)must be computed based on historical data.When the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of MHNBM.Here a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above shortcomings.For the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the model.At the same time,FWMNBM does not have to calculate the conditional probability between variables,thus it is less restricted by the number of training data samples.Compared with the MHNBM,the FWMNBM has better performance,and its effectiveness is validated through numerical cases of a simulation example and a practical case of the Zhoushan thermal power plant(ZTPP),China.
文献关键词：
中图分类号：
作者姓名：
Min Wang;Li Sheng;Donghua Zhou;Maoyin Chen
作者机构：
Department of Automation,Tsinghua University,Beijing 100084,China;College of Control Science and Engineering,China University of Petroleum(East China),Qingdao 266580,China;College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590
文献出处：
引用格式：
[1]Min Wang;Li Sheng;Donghua Zhou;Maoyin Chen-.A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant)[J].自动化学报（英文版）,2022(04):719-727
A类：
MHNBM,FWMNBM,ZTPP
B类：
Feature,Weighted,Mixed,Naive,Model,Monitoring,Anomalies,Fan,System,Thermal,Power,Plant,With,increasing,intelligence,integration,number,two,valued,variables,generally,stored,often,exist,large,scale,industrial,processes,However,these,cannot,effectively,handled,by,traditional,monitoring,methods,such,linear,discriminant,analysis,LDA,principal,component,partial,least,square,PLS,Recently,mixed,hidden,naive,Bayesian,model,developed,first,utilize,both,continuous,abnormality,Although,still,has,some,shortcomings,that,need,improved,For,greater,correlation,other,have,weights,which,guarantee,assigned,more,discriminating,In,addition,conditional,probability,xj,must,computed,historical,data,When,training,scarce,between,tends,uniformly,distributed,affects,performance,Here,novel,feature,weighted,overcome,above,correlated,class,makes,contribute,At,same,does,calculate,thus,less,restricted,samples,Compared,better,its,effectiveness,validated,through,numerical,cases,simulation,example,practical,Zhoushan,thermal,power,plant,China
AB值：
0.524731
相似文献
机标中图分类号，由域田数据科技根据网络公开资料自动分析生成，仅供学习研究参考。
