Association rules mining based analysis of consequential alarm sequences in chemical processes

作者：Wang, Jia; Li, Hongguang^*; Huang, Jingwen; Su, Chong

来源：Journal of Loss Prevention in the Process Industries, 2016, 41: 178-185.

DOI：10.1016/j.jlp.2016.03.022

• Fuzzy association rules
• Data mining
• Correlated alarms
• Alarm time sequences

In the context of industrial alarm rationalization, the analysis of consequential alarms is helpful for finding out root alarms so as to avoid alarm flooding. Motivated by this idea, this paper introduces a weighted fuzzy association rules mining approach to discovering correlated alarm sequences. Combining fuzzy sets, Apriori algorithms and alarm time series analysis, the algorithm does not search the entire item sets to find out root causes of consequential alarms. Furthermore, by transforming the association rules into fuzzy-driven causal knowledge bases and establishing the compatible fuzzy inference mechanism, a rationalized alarm topology is eventually created. Experimental results of a chemical plant show that the novel approach taking advantage of fuzzy inferences and data mining strategies is potentially effective to remove redundant alarm sequences.

• 出版日期2016-5
• 单位北京化工大学