Patterns Relevant to the Temporal Data-Context of an Alarm of Interest
Document Type
Book Chapter
Publisher
Information Science Reference
Faculty
Faculty of Computing, Health and Science
School
School of Computer and Security Science
RAS ID
9367
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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Comments
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. Available here.