Chapter

Advanced Data Mining and Applications

Volume 5139 of the series Lecture Notes in Computer Science pp 135-146

Analysis of Alarm Sequences in a Chemical Plant

  • Savo KordicAffiliated withThe School of Computer and Information Science, Edith Cowan University
  • , Peng LamAffiliated withThe School of Computer and Information Science, Edith Cowan University
  • , Jitian XiaoAffiliated withThe School of Computer and Information Science, Edith Cowan University
  • , Huaizhong LiAffiliated withThe School of Computer Science and Engineering, Wenzhou University Town

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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.

Keywords

Chemical plants Data mining and Correlated alarms