Pattern Extraction From Industrial Alarm Flood Sequences by a Modified CloFAST Algorithm

Abstract

Alarm systems are critical for process safety and efficiency of complex industrial facilities. However, the presence of alarm floods severely compromises the performance of alarm systems. To cope with alarm floods, data mining has been applied to discover interesting patterns from historical alarm data, and such patterns can be used for alarm suppression, root cause analysis, and decision supports. However, most existing methods ignored the timestamps in pattern extraction or obtained complete patterns with significant redundancy. In this article, a new method is proposed to extract alarm flood patterns using a modified CloFAST algorithm. The contributions are twofold: first, a closed alarm sequence mining approach is proposed based on the CloFAST algorithm with improvements to incorporate timestamps and tolerate alarm order switchings; second, a pattern distillation strategy is designed to merge similar alarm sequences and export compact alarm sequential patterns. The proposed method is capable of avoiding influences of order ambiguities and also minimizing the redundancy of extracted patterns. The effectiveness of the proposed method is demonstrated by an industrial case study involving alarm data from a large-scale industrial facility.