A distributed parallel alarm management strategy for alarm reduction in chemical plants

Abstract A distributed parallel alarm management strategy based on massive historical alarms and distributed clustering algorithm is proposed to reduce the number of alarms presented to operators in modern chemical plants. Due to the large and growing scale of historical alarms as the basis of analysis, it is difficult for traditional alarm management strategy to store and analyze all alarms efficiently. In this paper, by designing the row key and storage structure in a distributed extensible NoSQL database, the strategy spreads alarm data in a group of commercial machines, which ensures the capacity and scalability of the whole system. Meanwhile, Distributed Parallel Query Model (DPQM) proposed as a unified query model provides efficient query and better integration of distributed platform. Based on the characteristics of alarms and time-delay correlation of alarm occurrence, alarm similarity criteria are proposed to effectively identify repetitive and homologous alarms. In order to group massive alarm data, a new distributed clustering algorithm is designed to work concurrently in MapReduce frameworks. The test results using alarm data from real chemical plants show that the strategy is better than traditional method based on MySQL at system performance, and provides excellent redundant alarm suppression in both normal situation and alarm flooding situation.