Computing dependent industrial alarms for alarm flood reduction

Abstract

In the domain of process control, operators face the problem that more alarms are generated than can be physically addressed by a single operator. Such a situation is called alarm flood. The reasons for alarm floods are either badly designed alarm management systems (AMS) or causal dependent disturbances which either way, raise an alarm based on a single causal disturbance. These dependencies are difficult to recognize during the engineering of an AMS. This article presents an overview of an algorithm for the automatic alarm data analyzer (AADA). It is able to find possible and significant reasons for alarm floods by identifying the most frequent alarms and those causal alarms consolidating alarm-sequences. They are to be used to improve and to redesign an AMS, so that the alarm flood problem can be reduced at the end.