Open Set Online Classification of Industrial Alarm Floods with Alarm Ranking
Haniyeh Seyed Alinezhad, Jun Shang, Tongwen Chen
Abstract
Alarm floods can cause serious safety problems in complex industrial plants by overwhelming a plant operator with many alarm annunciations in a short time interval. In a plant operation, there can be alarm flood scenarios that correspond to previously unseen abnormal situations. Therefore, early online assistance for plant operators in both previously known and new situations is of great importance. The aim of this article is to develop an operator assistance system based on early classification of alarm floods and alarm ranking. A weighting method is developed to model alarm flood sequences as feature vectors while preserving key characteristics of them, including the temporal information of alarms. The proposed weighting strategy is defined by considering early classification accuracy and can also provide ranking of alarms according to their relevance to the abnormal situation. To handle the new alarm flood scenarios, an open set classification method based on a systematic similarity threshold estimation is proposed. The effectiveness of the proposed approach is evaluated by using the Tennessee Eastman benchmark.