A priority-aware sequential pattern mining method for detection of compact patterns from alarm floods

Abstract

Alarm floods are commonly present in modern complex industrial process, and usually degrade the performance of the alarm systems severely. In an alarm flood, the massive alarm messages may overwhelm plant operators and submerge critical alarms, which would further worsen the situation and lead to severe consequences. One effective way to deal with alarm flood situations is to mine meaningful alarm patterns that can be used to configure dynamic alarm suppression modules that suppress irrelevant alarms in alarm floods based on certain conditions or to help with online alarm prediction. Using the existing methods in literature, alarms of higher priorities could be easily overlooked in the pattern mining as they usually appear less frequent; thus, it makes the results become unreliable. Motivated by this issue, this paper proposes a new pattern mining method to extract priority-aware compact alarm patterns from historical alarm flood sequences. The main contributions of this study are twofold: (1) a priority-aware sequential pattern mining algorithm is designed to mine alarm sequential patterns using a closed sequential pattern mining algorithm with alarm priority information incorporated; (2) a pattern compression strategy is proposed to merge similar sequences into compact patterns, making it easier to search for alarm patterns. The effectiveness of the proposed pattern mining method is demonstrated by alarm data generated using a vinyl acetate monomer chemical plant model.