Paper Info
Title
Analysis of Alarm Sequences in a Chemical Plant
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.
Year
DOI
Venue
2008
10.1007/978-3-540-88192-6_14
ADMA
Keywords
Field
DocType
alarm pattern,novel approach,alarm sequences,cost-effective alarm system,data mining approach,simulation data,alarm rationalization,alarm event,chemical plant,alarm tag,alarm event log,oil and gas industry,cost effectiveness,data mining,chemical plants,control system
Data mining,Data filtering,Segmentation,ALARM,Computer science,Rationalization (psychology),Chemical plant,Distributed control system
Conference
Volume
ISSN
Citations 
5139
0302-9743
4
PageRank 
References 
Authors
0.44
11
4
Name
Order
Citations
PageRank
Savo Kordic140.78
Peng Lam251.13
Jitian Xiao3258.27
Huaizhong Li417718.16