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. (C) 2016 Elsevier Ltd. All rights reserved.