Frequent Alarm Pattern Mining of Industrial Alarm Flood Sequences by an Improved PrefixSpan Algorithm

Abstract

Alarm systems are essential to the process safety and efficiency of complex industrial facilities. However, with the increasing size of plants and the growing complexity of industrial processes, alarm flooding is becoming a serious problem and posing challenges to alarm systems. Extracting alarm patterns from an alarm flood database can assist with an alarm root cause analysis, decision support, and the configuration of an alarm suppression model. However, due to the large size of the alarm database and the problem of sequence ambiguity in the alarm sequence, existing algorithms suffer from excessive computational overhead, incomplete alarm patterns, and redundant outputs. In order to solve these problems, we propose an alarm pattern extraction method based on the improved PrefixSpan algorithm. Firstly, a priority-based pre-matching strategy is proposed to cluster similar sequences in advance. Secondly, we improved PrefixSpan by considering timestamps to tolerate short-term order ambiguity in alarm flood sequences. Thirdly, an alarm pattern compression method is proposed for the further distillation of pattern information in order to output representative alarm patterns. Finally, we evaluated the effectiveness and applicability of the proposed method by using an alarm flood database from a real diesel hydrogenation unit.