A Feature Weighted Mixed Naive Bayes Model for Monitoring Anomalies in the Fan System of a Thermal Power Plant

作     者：Min Wang Li Sheng Donghua Zhou Maoyin Chen Min Wang;Li Sheng;Donghua Zhou;Maoyin Chen

作者机构：IEEE the Department of AutomationTsinghua University the College of Control Science and EngineeringChina University of Petroleum(East China) the College of Electrical Engineering and AutomationShandong University of Science and Technology 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2022年第9卷第4期

页      面：719-727页

核心收录：

学科分类：080802[工学-电力系统及其自动化] 0808[工学-电气工程] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(62033008 61873143) 

主　　题：Abnormality monitoring continuous variables feature weighted mixed naive Bayes model(FWMNBM) two-valued variables thermal power plant 

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 ***,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)***,a mixed hidden naive Bayesian model(MHNBM)is developed for the first time to utilize both two-valued and continuous variables for abnormality *** the MHNBM is effective,it still has some shortcomings that need to be *** 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 *** addition,the conditional P(x j|x j′,y=k)probability must be computed based on historical *** the training data is scarce,the conditional probability between continuous variables tends to be uniformly distributed,which affects the performance of *** a novel feature weighted mixed naive Bayes model(FWMNBM)is developed to overcome the above *** the FWMNBM,the variables that are more correlated to the class have greater weights,which makes the more discriminating variables contribute more to the *** 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 *** 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.