Mining Sequential Alarm Pattern Based on the Incremental Causality PrefixSpan Algorithm

Abstract

Alarm flood is the main reason for the alarm system failure in the modern industry, so suppressing the subsequent alarms caused by anomalous propagation during the alarm flooding is an important issue. An incremental causality prefixSpan malgorithm is proposed to mine the frequent and sequence alarm patterns from the alarm flood, and dynamically update the old alarm mode from the new database. First, the alarm event is re-defined and the detection of alarm flood is formulated. The frequent alarm pattern with causality is expressed with the orderly and time delay features of sequential alarms. Under the new definition of alarm pattern, causality prefixSpan algorithm is proposed by introducing the time constraints, i.e., the causality. Furthermore, a new incremental mining strategy is co-implemented on the improved algorithm. When new alarm flooding data occurs, the old alarm modes are quickly updated without re-scanning the entire changing database. The incremental strategy makes the proposed method easily implemented in the real operation. The effectiveness of the proposed method is verified with the synthetic data sets and Tennessee-Sterman (TE) process.