Analysis of Alarm Sequences in a Chemical Plant

Authors:
Savo Kordic;Peng Lam;Jitian Xiao;Huaizhong Li
Affiliations:
The School of Computer and Information Science, Edith Cowan University, Perth, 6050;The School of Computer and Information Science, Edith Cowan University, Perth, 6050;The School of Computer and Information Science, Edith Cowan University, Perth, 6050;The School of Computer Science and Engineering, Wenzhou University Town, Zhejiang, China 325035
Venue:
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
Year:
2008

Citing 5
Cited 2

Mining frequent patterns without candidate generation

SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining Sequential Patterns: Generalizations and Performance Improvements

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Knowledge Discovery from Telecommunication Network Alarm Databases

ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
An Online Algorithm for Segmenting Time Series

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
MAFIA: A Maximal Frequent Itemset Algorithm

IEEE Transactions on Knowledge and Data Engineering

Mining redundant industrial alarm occurrences with association rules extraction and complex networks modeling

Journal of Computational Methods in Sciences and Engineering
Predictive combinations of monitor alarms preceding in-hospital code blue events

Journal of Biomedical Informatics

Quantified Score

Hi-index	0.00

Visualization

Abstract

Oil and gas industries need secure and cost-effective alarm systems to meet safety requirements and to avoid problems that lead to plant shutdowns, production losses, accidents and associated lawsuit costs. Although most current distributed control systems (DCS) collect and archive alarm event logs, the extensive quantity and complexity of such data make identification of the problem a very labour-intensive and time-consuming task. This paper proposes a data mining approach that is designed to support alarm rationalization by discovering correlated sets of alarm tags. The proposed approach was initially evaluated using simulation data from a Vinyl Acetate model. Experimental results show that our novel approach, using an event segmentation and data filtering strategy based on a cross-effecttest is significant because of its practicality. It has the potential to perform meaningful and efficient extraction of alarm patterns from a sequence of alarm events.