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In distributed systems, the scale is very large, and the structure is complex, which make the collection of fault data of the systems very hard. In order to diagnose the distributed systems efficiently, this paper proposed an incomplete fault data based fault diagnosis approach in distributed systems. Firstly, we diagnosed the distributed systems with the asymmetric comparison-based method. Secondly, we transformed the fault diagnosis problem into a binary classification problem. Finally, we applied the linear support vector machine to classify the nodes into two statuses based on partial syndromes. The simulation experiments show that, the proposed approach is efficient in recognizing fault nodes. In addition, in order to improve the execution efficiency of the proposed approach, we can use incomplete syndromes to refine the fault nodes in a certain sets.