Abstract: Abstract： The complexity and diversity of the control cluster system lead to the easy failure of the cluster system，which reduces the availability of the cluster system. In view of the problems in the existing fault detection methods，such as low detection efficiency and accuracy，and difficulty in effective automatic identification of fault types，in this paper，a control cluster fault detection method based on an improved adaptive Arithmetic Optimization Algorithm-Multi-class SVM （AOA-MSVM） is proposed to detect cluster faults in order to improve the availability of the cluster system. Firstly，the local linear embedding algorithm is used to reduce the dimensionality of the system information detected in the cluster system. Then，according to the characteristics of multiple kinds of faults in the cluster system，the method of one-to-many support vector machine is used to build a fault detection model to improve the ability of fault detection. Finally，the improved adaptive arithmetic optimization algorithm is used to obtain the optimal solution of the model parameters. A high availability control cluster system is set up for comparative experiments. The experimental results show that the proposed fault detection method has higher detection efficiency and accuracy and can effectively identify the fault type.

Key words: Key words： fault detection, cluster, multi-class support vector machine, arithmetic optimization algorithm