A distributed parallel alarm management strategy for alarm reduction in chemical plants. (October 2015)

• Record Type: Journal Article
• Authors: Li, Dewen; Hu, Jinghong; Wang, Hao; Huang, Wenjun
• Is Part Of: Journal of process control. Volume 34(2015:Oct.)
• Page Start: 117
• Page End: 125
• Publication Date: 2015-10
• Subjects: Alarm flooding -- Distributed parallel alarm management strategy -- Massive alarm storage -- Distributed Parallel Query Model -- Distributed alarm clustering algorithm -- Alarm similarity criterion; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2015.07.008
• Languages: English
• ISSNs: 0959-1524

Abstract

Highlights: The strategy groups repetitive and homologous alarms in chemical plants. It selects urgent alarms with different causes to display them in alarm list. HBase is used to store massive alarms in a distributed and extensible manner. Distributed Parallel Query Model is proposed as an efficient query model. A distributed clustering algorithm is designed to analyze alarms concurrently. 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.