Analysis of Alarm Sequences in a Chemical Plant
Document Type
Conference Proceeding
Publisher
Springer Verlag
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Security Science
RAS ID
5956
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-effect test 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.
DOI
10.1007/978-3-540-88192-6_14
Comments
Kordic, S., Lam, C. P., Xiao, J., & Li, H. (2008). Analysis of alarm sequences in a chemical plant. Proceedings of International Conference on Advanced Data Mining and Applications. (pp. 135-146). Chengdu, China. Springer Verlag. Available here