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In industrial environment, fan vibration fault often needs manual diagnosis, which is inefficient and difficult to complete real-time calculation and online analysis and judgment. To solve the above problems, a membrane clustering algorithm is proposed in this paper, which can be used for on-line intelligent diagnosis of fan vibration faults. The algorithm introduces the membrane computing method into clustering, and uses the probability model to update the population method to optimize the best clustering center. The algorithm first carries out clustering experiments on multiple data sets, and the experimental results show that the algorithm overcomes the shortcomings of the unstable clustering results and poor quality of the clustering algorithm. Then it is applied to the on-line diagnosis system of fan vibration fault. The results show that the method can meet the on-line intelligent diagnosis requirement of fan vibration fault and can also be applied to the on-line intelligent diagnosis of vibration fault of other kinds of equipment.