Associative Data Mining for Alarm Groupings in Chemical Processes

Complex industrial processes such as nuclear power plants, chemical plants and petroleum refineries are usually equipped with alarm systems capable of monitoring thousands of process variables and generating tens of thousands of alarms which are used as mechanisms for alerting operators to take actions to alleviate or prevent an abnormal situation. Overalarming and a lack of configuration management practices have often led to the degradation of these alarm systems, resulting in operational problems such as the Three-Mile Island accident. In order to aid alarm rationalization, this paper proposed an approach that incorporates a context-based segmentation approach with a data mining technique to find a set of correlated alarms from historical alarm event logs. Before the set of extracted results from this automated technique are used they can be evaluated by a process engineer with process understanding. The proposed approach is evaluated initially using simulation data from a Vinyl Acetate model. The approach is cost effective as any manual alarm analysis of the event logs for identifying primary and consequential alarms could be very time and labour intensive.