Abstract
AbstractEvent correlation analysis is a data‐mining method that detects statistical similarities among discrete occurrences of alarms or operations. By grouping correlated events based on degree of similarity, a policy for reducing alarms and operations can be designed more easily than by analyzing individual alarms and operations. In this study, event correlation analysis was applied to event log data at an Idemitsu Kosan ethylene plant in Japan to reduce the number of alarms and operations, where events are defined by both tag names and types of tag. The obtained results were helpful for identifying unnecessary alarms and operations, such as sequential alarms and routine operations, buried in a lot of noisy plant data. Copyright © 2011 Curtin University of Technology and John Wiley & Sons, Ltd.
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