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Quantitative analysis of a process industry’s operating status of based on DCS data set
Author(s) -
Qian Chen,
Kai Sun
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1043/3/032066
Subject(s) - classifier (uml) , data mining , computer science , training set , artificial intelligence , real time computing
The process industry represented by energy chemical enterprise is a typical distributed and complex electromechanical system. Its distributed control system (DCS) records overall operating status information of a process industry. It is a challenge to quantitative operating status through analyzing DCS data set. This paper proposed a novel method to calculate fault score of system’s operating status by classifying its DCS data set and forming a classifier matrix. Firstly, define a classifier matrix depends on the baseline of the DCS data. Secondly, calculate the classifier matrix to obtain the fault score. The higher fault score means the lower security of the system. Last but not least, a period of the system operating status trend could be observed by plotting the fault score. A case study of a real chemical plant is introduced to verify the validation of the method.

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