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Alarm management efforts have recently intensified in the world, and there are many tools based on a guideline EEMUA191 to evaluate alarm system performance. However, the improvement and rationalization of alarm systems have not made satisfactory progress, because these tools usually just provide with alarm system performance metrics but not useful information to improve and rationalize alarm systems. In this paper, a novel method for performance improvement of IPL2&3 including alarm system using the event correlation analysis was proposed.

Event correlation analysis was applied alarm data at an Idemitsu Kosan ethylene plant in Japan to reduce the number of plant alarms. This 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 can be designed more easily than by analyzing individual alarms and operations. By using event correlation analysis, we were able to divide event data of alarms or operations of the ethylene plant into a limited number of groups. The results of the analysis were helpful for identifying unnecessary alarms, such as sequential alarms, buried in a lot of noisy plant data. This method is useful for reducing the number of alarms.