Document Type

Book Chapter

Publisher

Information Science Reference

Faculty

Computing, Health and Science

School

Computer & Security Science

RAS ID

9367

Comments

This chapter was originally published as: Kordic, S. , Lam, C. P., Xiao, J. , & Li, H. (2010). Patterns Relevant to the Temporal Data-Context of an Alarm of Interest. In A B M Ali & Yang Xiang (Eds.). Dynamic and Advanced Data Mining for Progressing Technological Development: Innovations and Systemic Approaches (pp. 18-39). Location: Information Science Reference.

Abstract

The productivity of chemical plants and petroleum refineries depends on the performance of alarm systems.

Alarm history collected from distributed control systems (DCS) provides useful information about past

plant alarm system performance. However, the discovery of patterns and relationships from such data

can be very difficult and costly. Due to various factors such as a high volume of

tags, manual identification and analysis of alarm logs is usually a labor-intensive and time-consuming

task. This chapter describes a data mining approach for analyzing alarm logs in a chemical plant. The

main idea of the approach is to investigate dependencies between alarms effectively by considering the

temporal context and time intervals between different alarm types, and then employing a data mining

technique capable of discovering patterns associated with these time intervals. A prototype has been

implemented to allow an active exploration of the alarm grouping data space relevant to the tags of

interest.

alarm data (especially

during plant upsets), huge amounts of nuisance alarms, and very large numbers of individual alarm

 
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