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Outlier screening for ironmaking data on blast furnaces

International Journal of Minerals,Metallurgy and Materials 2021年第6期28卷 1001-1010页

作者： Jun Zhao Shao-fei Chen Xiao-jie Liu Xin Li Hong-yang Li Qing Lyu College of Metallurgy Northeastern UniversityShenyang 110819China Tangshan Branch of HBIS Group Co. Ltd.Tangshan 063020China College of Metallurgy and Energy North China University of TechnologyTangshan 063210China

Blast furnace data processing is prone to problems such as outliers.To overcome these problems and identify an improved method for processing blast furnace data,we conducted an in-depth study of blast furnace data.Based on data samples from selected iron and steel companies,data types were classified according to different characteristics;then,appropriate methods were selected to process them in order to solve the deficiencies and outliers of the original blast furnace data.Linear interpolation was used to fill in the divided continuation data,the Knearest neighbor(KNN)algorithm was used to fill in correlation data with the internal law,and periodic statistical data were filled by the average.The error rate in the filling was low,and the fitting degree was over 85%.For the screening of outliers,corresponding indicator parameters were added according to the continuity,relevance,and periodicity of different data.Also,a variety of algorithms were used for processing.Through the analysis of screening results,a large amount of efficient information in the data was retained,and ineffective outliers were eliminated.Standardized processing of blast furnace big data as the basis of applied research on blast furnace big data can serve as an important means to improve data quality and retain data value.

关键词： blast furnace data missing outliers data processing data mining

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Fault Prognosis for data Incomplete Systems: A Dynamic Bayesian Network Approach

Fault Prognosis for Data Incomplete Systems: A Dynamic Bayes...

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第24届中国控制与决策会议

作者： Zhu Jinlin, Zhang Zhengdao * Key Laboratory of Advanced Process Control for Light Industry, Ministry of Education, Jiangnan University, Wuxi 214122

For the cases that data samples are partially missing in control systems, analysis are given to determine the type of missing data mechanisms, then a dynamic Bayesian network approach is used to model the general fault prognosis problem in control systems, after that we proposed the method of dynamic Bayesian network to deal with real-time fault prognosis of nonlinear systems with missing data. Our approach is demonstrated on a benchmark continuous stirred tank reactor （CSTR） problem, with which we show the process of constructing the dynamic Bayesian network model and use the model for the simulation of fault prognosis. Results show that though data samples are noisy and partially missing, combined with effective treatment of missing data, dynamic Bayesian networks can efficiently predict the system failures.

关键词： Fault prognosis data missing Dynamic Bayesian network

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Fault Diagnosis Based on Bayesian Networks for the data Incomplete Industrial System

Fault Diagnosis Based on Bayesian Networks for the Data Inco...

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第三十届中国控制会议

作者： ZHU Jinlin,ZHANG Zhengdao~* The Key Laboratory of Advanced Process Control for Light Industry,Ministry of Education,Jiangnan University,Wuxi 214122, P.R.China

＜正＞In the data-incomplete industrial systems,the existing data-driven fault diagnosis techniques cannot be applied directly due to the missing of sampled data.In this paper,we propose a method based on bayesian networks to realize the fault diagnosis of systems with incomplete sample data.Our method uses the Expectation-Maximization（EM） algorithm to estimate the missing part of incomplete sample data,then selects the features based on the mutual information technique,and finally, constructs the bayesian network classifier to achieve the fault diagnosis of systems.We used the Tennessee Eastman Process as the simulation model,and analyzed the diagnostic performance under different degrees of missing data.Both the normal case and three faults had been considered in the simulation.Compared with the data-complete case,our method achieved a good diagnosis performance in the case within 10%rate of missing sample data.

关键词： Bayesian networks data missing Fault diagnosis Tennessee Eastman Process（TEP）

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