Detection of Sequential Alarm Patterns in Complex Industrial Facilities Using ClaSP and Top-K Algorithms

Abstract

In modern complex industrial processes, due to poor rationalization of alarm systems and the complexity of process interconnections, alarm floods are commonly present. Alarm floods are also identified as the main causes of many industrial accidents. One valid approach to deal with alarm floods is to mine meaningful alarm sequential patterns from alarm floods. These identified patterns can help to analyze root causes or to configure dynamic alarming modules. In this paper, a method based on the combination of ClaSP and Top-K is proposed to mine interesting alarm sequential patterns from historical alarm data. Main contributions of this study are twofold: 1) A pattern mining approach is adapted to mine interesting patterns from alarm flood sequences; 2) A pattern compression strategy is proposed to reduce pattern redundancy. A case study is presented to demonstrate the effectiveness of the proposed method.