Abstract：The existing alarm information correlation analysis methods take no consideration of the alarm significance, so they are unable to reflect individual differences between the alarms. In order to solve the problem, a new alarm information relevance mining mechanism based on wavelet neural network is proposed in this paper. The three key attributes of alarm information, alarm level, alarm type and alarm equipment type are considered as the inputs of the wavelet neural network respectively. Further, the weight corresponds to the importance of individual attribute, which can be determined reasonably by training with history sample. Finally, the association rules can be mined accurately. Results show that the proposed algorithm can consider multiple influence factors and history sample comprehensively, and the obtained weight can scientifically reflect the importance of the alarms; moreover, the association rules can reflect the correlation between the alarms more accurately.
2014, Vol. 36 
