Modern distributed control systems and the advancements in the communication and industrial computing technologies have given the convenience to monitor each and every possible point in an industry. Alarms on each of these monitored variables can be configured through computer software and requires a very little effort. In large industries, often operators experience a very high number of alarms within a very short period of time. An incident when the number of alarms annunciated is more like to exceed operator’s response capability is referred as an alarm flood.

A very general way to define an alarm flood is based on the alarm rate per 10 minutes. An alarm rate greater than 10 per 10 minutes is commonly called an alarm flood. Below a plot of alarm rate per 10 minutes versus time is shown where alarm floods can be detected when the plot crosses an alarm rate of 10 per 10minutes.

It has been seen that many of these alarm floods are very similar to each other in terms of the patterns of alarm annunciation. Our research is to analyze these patterns and cluster similar alarm floods in to groups. Similarities in alarm patterns can be interpreted as similarities in fault propagations within a process. This study will aid to develop the methods to prevent such groups alarm floods which are regular in time and due to a same set of interrelated process variables.

Different pattern analysis techniques were applied to investigate pairwise similarity and were clustered using an unsupervised clustering algorithm in our study. In following figure, a case study on 39 alarm floods from real industrial data is shown where the similar alarm floods were clustered together and indicated by the clusters of darker pixels.

A further investigation on these groups of alarm floods were also carried out to validate the similarity in alarm patterns. Following few of such alarm flood sequences are shown to illustrate the similarity in patterns of alarm annunciations.

Similarity investigation on alarm floods categories different sets of alarms which are raised in a sequence. A rationalization or a causality analysis of these alarm sets will help to prevent such large sequence of alarms in future and help to reduce the number alarm floods in process industries.