Data-mining-based system for prediction of water chemistry faults

被引：23

作者：

Kusiak, A ^{[1
]}

Shah, S ^{[1
]}

机构：

[1] Univ Iowa, Dept Mech & Ind Engn, Intelligent Syst Lab, Iowa City, IA 52242 USA

来源：

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS | 2006年 / 53卷 / 02期

关键词：

alarm system; data merging; data mining; fault prediction; hierarchical decision making; power plant; time lag; water chemistry;

D O I：

10.1109/TIE.2006.870706

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Fault monitoring and prediction is of prime importance in process industries. Faults are usually rare, and, therefore, predicting them is difficult. In this paper, simple and robust alarm-system architecture for predicting incoming faults is proposed. The system is data driven, modular, and based on data mining of merged data sets. The system functions include data preprocessing, learning, prediction, alarm generation, and display. A hierarchical decision-making algorithm for fault prediction has been developed. The alarm system was applied for prediction and avoidance of water chemistry faults (WCFs) at two commercial power plants. The prediction module predicted WCFs (inadvertently leading to boiler shutdowns) for independent test data sets. The system is applicable for real-time monitoring of facilities with sparse historical fault data.

引用

页码：593 / 603

页数：11