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

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 P(x j|x j′,y=k)probability 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.

Naive Bayes for value difference metric

The value difference metric （VDM） is one of the best-known and widely used distance functions for nominal attributes. This work applies the instance weighting technique to improve VDM. An instance weighted value difference met- ric （IWVDM） is proposed here. Different from prior work, IWVDM uses naive Bayes （NB） to find weights for train- ing instances. Because early work has shown that there is a close relationship between VDM and NB, some work on NB can be applied to VDM. The weight of a training instance x, that belongs to the class c, is assigned according to the dif- ference between the estimated conditional probability P（c／x） by NB and the true conditional probability P（c／x）, and the weight is adjusted iteratively. Compared with previous work, IWVDM has the advantage of reducing the time complex- ity of the process of finding weights, and simultaneously im- proving the performance of VDM. Experimental results on 36 UCI datasets validate the effectiveness of IWVDM.