With the continuous improvement in the efficiency of the heavy-haul railway freight transportation, the pressure on on-site maintenance is increasing. In-depth research on fault characteristics carries significant importance for fault scientific judgment and fault prevention. This study proposes an efficient association rule mining (ARM) algorithm, HM-RDHP, for analyzing fault data from heavy-haul railway freight trains. The algorithm introduces distributed parallel computing technology, integrating the MapReduce framework and HBase on the Hadoop platform to process large volumes of complex fault data efficiently. Experimental results show that the HM-RDHP algorithm can efficiently uncover hidden patterns and associations within the fault data of heavy-haul railway freight trains. The mined association rules provide a valuable reference model to aid in predictive maintenance and fault prevention strategies for freight train maintenance departments.