In order to find out root alarms so as to avoid alarm flooding in industrial processes

this paper suggests a fuzzy weighted association rules mining method which integrates fuzzy sets

the Apriori mining algorithm and time-series analysis to extract the association rules from alarm sequence data. Considering the temporal characteristics and similarity attributes of the alarm data

the similarity degrees are assigned as the weights of the association rules

enhancing the algorithmic efficiency and accuracy. Further

in contrast to quantitative knowledge representations

the resultant fuzzy association rules are easier identified to the operators. Practical data of an industrial process are employed to illustrate the benefits of the proposed method.