Title

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

Document Type

Article

Peer Reviewed

1

Publication Date

1-1-2006

Journal/Book/Conference Title

Industrial Electronics, IEEE Transactions on

Volume

53

Abstract

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.

Keywords

Sustainability, 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.

Published Article/Book Citation

Industrial Electronics, IEEE Transactions on, 53:2 (2006) pp.593-603.



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URL

http://ir.uiowa.edu/cee_pubs/363