A Modified Bag-of-Words Representation for Industrial Alarm Floods
Haniyeh Seyed Alinezhad, Jun Shang, Tongwen Chen · 2022
Alarm floods pose a serious threat to the safety of complex industrial plants by overloading an operator’s cognitive abilities with a large number of alarms in a short period of time. Therefore, the development of methods to assist operators in handling alarm floods is of great importance. In this paper, an operator assistance system is developed that relies on similarity analysis of alarm floods and alarm scoring. A vector representation called the Modified Bag of Words is proposed to turn alarm floods into feature vectors, which are then used in a clustering algorithm for similarity analysis. An alarm weighting strategy reflecting the key features of alarm floods, such as temporal information, is proposed, which provides alarm ranking to assist operators in identifying alarms relevant to specific abnormal situations. Utilizing the Tennessee Eastman process benchmark, a qualitative assessment of the proposed approach is conducted.