A grouping and suppression strategy for correlated alarms based on process data

To solve the common problems of the alarm system in the process industry, such as low efficiency and frequent interlocking alarm, this paper proposes a grouping and suppression strategy for correlated alarms based on process data. The Ward Method was used in this strategy for cluster analysis of the process data, and distance coefficient was the consideration to group the alarm variables that may related. Quantization regulations of alarm priority were introduced to identify the alarms needing response or suppression in an alarm group via analyzing the accurate priority score from both response time and severity of consequences. It is helpful for controllers to recognize the correlation among alarms, and improve the response efficiency. The strategy yields an effective alarm management that can help plant owners and operators to comply with the standards for alarm management such as ANSI/ISA 18.2 (2009) and EEMUA 191 (2007) which set limits on the number of alarms per unit time for an operator. The effectiveness of the approach is illustrated by successful application in TE process model where a significant reduction of alarms has been achieved.