Patterns Relevant to the Temporal Data-Context of an Alarm of Interest

Patterns Relevant to the Temporal Data-Context of an Alarm of Interest

Savo Kordic (Edith Cowan University, Australia), Chiou Peng Lam (Edith Cowan University, Australia), Jitian Xiao (Edith Cowan University, Australia) and Huaizhong Li (Wenzhou University, China)
DOI: 10.4018/978-1-60566-908-3.ch002
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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 alarm data (especially during plant upsets), huge amounts of nuisance alarms, and very large numbers of individual alarm 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.
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Temporal data mining (Roddick & Spiliopoulou, 2002) is concerned with the analysis of sequences of events or itemsets in large sequential databases, where records are either chronologically ordered lists of events or indexed by transaction-time, respectively. The task of temporal data mining is different from the non-temporal discovery of relationships among itemsets such as association rules (Agrawal, Imielinski, & Swami, 1993), since of particular interest in temporal data mining is the discovery of causal relationships and temporal patterns and rules. Thus techniques for finding temporal patterns take time into account by observing differences in the temporal data.

In temporal data mining, the discovery process usually includes sliding time windows or time constraints. Srikant & Agrawal (1996) developed the GSP algorithm which generalizes the sequential pattern framework by including the maximum and minimum time periods between adjacent elements of the sequential patterns and allows items to be selected within a user-specified transaction-time window. The idea of Zaki (2000) was to incorporate into the mining process additional constraints such as the maximum length of a pattern, and constraints on an item’s inclusion in a sequence.

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