Application of Multivariate Statistics for Efficient Alarm Generation

Abstract

Ever since the introduction of Distributed Control Systems (DCS), there has been little motivation for limiting the number of alarms that could be configured for process monitoring. As a result, plant operators are overwhelmed with many alarms during process upsets. Although a handful of these alarms are informative, many of them are a nuisance to the operator. However, all the alarms need to be acknowledged. Generally, check limits on univariate alarms are based on statistical quality control (three sigma limits, also known as Shewhart charts). While annunciating a univariate alarm on a particular variable, the information from other variables is often ignored. Modern day process plants have variables which are highly correlated. This correlation structure can be exploited in the efficient management of alarms. This work demonstrates the advantages of monitoring the PCA based T2 and Q statistic over individual process variables. Monitoring these higher level statistics will not only reduce the false alarm and missed alarm rates but also reduces the detection latency which is one of the main drawbacks of monitoring a filtered variable. Two simulation examples are shown to illustrate the utility of the proposed method.