Performance Monitoring of Industrial Plant Alarm Systems Using Event Correlation Analysis

Performance Monitoring of Industrial Plant Alarm Systems
 Using Event Correlation Analysis

Tsutomu Takai and Masaru Noda

Graduate School of Information Science, Nara Institute of Science and Technology

Abstract

Alarm systems are essential for ensuring plant safety and effective operations, and the management efforts aiming at maintaining and improving alarm systems have recently intensified in process industries.  The alarm management currently in place for existing plants is basically a CAPDo approach that begins by evaluating the alarm system performance, and the result is quite important for finding issues to be effectively maintained and improved.  The conventional methods for evaluating alarm rates, alarm and event distributions, standing alarm times, etc. in quantity, are still a long way away from effectively evaluating alarm systems, because they do not evaluate each alarm as a signal requiring operator attention.  The Engineering Equipment & Materials Users' Association (EEMUA, 2007) says that every alarm presented to an operator should be useful and relevant to the operator.  Thus, the relationship between an alarm and the operator response is thought of as a new key performance indicator (KPI) of the performance of an alarm system.

Takai et al. (2010) proposed an evaluation method focusing on the relationship between them using an operator questionnaire. A questionnaire is reasonable at the working-level of plant operations, but questionnaire-based evaluation is often subjective, and may contain bias arising from the format of the questionnaires or from the individual respondents.  We propose a new KPI for evaluating the alarm system performance and a calculation method using an event correlation analysis (Nishiguchi and Takai, 2010) that is a data mining method to quantify the degree of similarity and time lag between two events using the cross correlation function, from the event log data, which is composed of discrete alarms and operator actions at the time they occur.

Event pairs separated by consistent time intervals are considered related in the event correlation analysis, since the length of the time lags is determined by factors such as the process dynamics and operator reaction time. A similarity measure between all the event pairs is calculated from the event log data along with the probability distribution of the correlation regarding the independent event pairs. The similarities and intervals for all the combinations between event pairs are calculated, and the groups with highly related events are identified by the pair-wise similarities using the hierarchical clustering method.  Then, each alarm is assayed if the based alarm initiates the required corrective actions after the alarm occurs in the group.  The relevant alarm rate is defined as the ratio of the number of relevant alarms from all the alarms. The effectiveness of the proposed method was validated with actual plant event data and simulation results.