  With the increasing intelligence and integration, a great number of
two-valued variables (generally stored in the form of 0 or 1 value) often exist
in large-scale industrial processes. However, these variables cannot be
effectively handled by traditional monitoring methods such as LDA, PCA and PLS.
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 MHNBM is effective, it still has some shortcomings that
need to be improved. For MHNBM, the variables with greater correlation to other
variables have greater weights, which cannot guarantee greater weights are
assigned to the more discriminating variables. In addition, the conditional
probability must be computed based on the 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 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
MHNBM, FWMNBM has better performance, and its effectiveness is validated
through the numerical cases of a simulation example and a practical case of
Zhoushan thermal power plant (ZTPP), China.
