Abstract: The operation and maintenance management of the power equipment (PE) mainly includes fault analy- sis，active early-warning and differentiated operation and maintenance． In the context of massive data with multiple time scales and multiple time and space dimensions in the process of grid operation，data mining technology was ap- plied for PE operation and maintenance management． The one-dimensional fault feature was extracted from fault in- formation by K-means clustering algorithm． Then，Apriori algorithm was employed to mine association rules of dif- ferent failure modes and establish key performance matrix． The spatial-temporal characteristics were analyzed based on high-dimensional random matrix theory (ＲMT)． Afterwards，one-dimensional and multi-dimensional fault fea- tures were combined based on D-S evidence theory so that the fault diagnosis criteria of PE was obtained． At the same time，comprehensively considering the PE operating state and the variation for power users，health index and importance index of equipment were established，which could help to significantly reduce the decision-making risk of PE operation and maintenance． The result of simulation proves the effectiveness of the proposed method．

Key words: power equipment, data mining, association rule mining, operation and maintenance management, de- cision-making risk