For spatiotemporal characteristics of the equipment failure data, a fast miner algorithm is proposed based on apriori algorithm. From a global perspective of equipment failure, equipment failure spatiotemporal co-occurrence pattern is defined. The pattern is described with several parameters, which are failure-instances spatial participation ratio (SPR), candidate spatial co-location pattern participation index (SPI), failure-types temporal participation degree (TPD) and failure-types temporal participation index (TPI). By simulating, analyzing and comparing with execution efficiency between the fast miner algorithm and na?ve miner algorithm, we find that the fast miner algorithm has higher execution efficiency when the failure equipment data is large and contains much more noise.