Process-data-warehousing-based operator support system for complex production technologies

被引:4
作者
Pach, FP [1 ]
Feil, B [1 ]
Nemeth, S [1 ]
Arva, P [1 ]
Abonyi, J [1 ]
机构
[1] Univ Veszprem, Dept Proc Engn, H-8201 Veszprem, Hungary
来源
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS | 2006年 / 36卷 / 01期
关键词
data mining; data warehousing; decision support system (DSS); heterogenous data integration; human-system interaction; Kalman Filter; neural network (NN); process monitoring;
D O I
10.1109/TSMCA.2006.859105
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Process manufacturing is increasingly being driven by market forces, customer needs, and perceptions, resulting in more and more complex multiproduct manufacturing technologies. The increasing automation and tighter quality constraints related to these processes make the operator's job more and more difficult. This makes decision support systems (DSSs) for the operator more important than ever before. A traditional operator support system (OSS) focuses only on specific tasks that are performed. In the case of complex processes, the design of an integrated information system is extremely important. The proposed data-warehouse-based OSS makes possible linking complex and isolated production units based on the integration of the heterogenous information collected from the production units of a complex production process. The developed OSS is based on a data warehouse designed by following the proposed focus-on-process data-warehouse-design approach, which means stronger focus on the material and information flow through the entire enterprise. The resulting,OSS follows the process through the organization instead of focusing separate tasks of the isolated process units. For human-computer interaction, front-end tools have been worked out, where exploratory data analysis and advanced multivariate statistical models are applied to extract the most informative features of the operation of the technology. The concept is illustrated by an industrial case study, where the OSS is designed for the monitoring and control of a high-density polyethylene (HDPE) plant.
引用
收藏
页码:136 / 153
页数:18
相关论文
共 44 条
[1]   Modified Gath-Geva clustering for fuzzy segmentation of multivariate time-series [J].
Abonyi, J ;
Feil, B ;
Nemeth, S ;
Arva, P .
FUZZY SETS AND SYSTEMS, 2005, 149 (01) :39-56
[2]   Process analysis and product quality estimation by Self-Organizing Maps with an application to polyethylene production [J].
Abonyi, J ;
Nemeth, S ;
Vincze, C ;
Arva, P .
COMPUTERS IN INDUSTRY, 2003, 52 (03) :221-234
[3]  
AJTONYI I, 2001, P DISTR CONTR SYST 7
[4]   Comparison of steady state and integral dynamic data reconciliation [J].
Bagajewicz, MJ ;
Jiang, QY .
COMPUTERS & CHEMICAL ENGINEERING, 2000, 24 (11) :2367-2383
[5]  
BALLARD C, 1998, DATA MODELING TECHNI, P25
[6]   Development of data reconciliation for dynamic nonlinear system: application the polymerization reactor [J].
Barbosa, VP ;
Wolf, MRM ;
Fo, RM .
COMPUTERS & CHEMICAL ENGINEERING, 2000, 24 (2-7) :501-506
[7]   Semantic integration of heterogeneous information sources [J].
Bergamaschi, S ;
Castano, S ;
Vincini, M ;
Beneventano, D .
DATA & KNOWLEDGE ENGINEERING, 2001, 36 (03) :215-249
[8]   On the regularization of dynamic data reconciliation problems [J].
Binder, T ;
Blank, L ;
Dahmen, W ;
Marquardt, W .
JOURNAL OF PROCESS CONTROL, 2002, 12 (04) :557-567
[9]  
CAPOCACCIA G, 2001, P DISTR CONTR SYST 7
[10]  
CHEREMISINOFF NP, 1989, ENCY ENG MAT A, V1