中文摘要：

在水电机组轴心轨迹识别研究中，为解决传统支持向量机方法中特征参数无法自适应选择而导致分类性能不高、计算时间过长等问题，提出混合人工蜜蜂群算法特征参数同步优化支持向量机（HABC—SVM）的轴心轨迹识别方法。将人工蜜蜂群算法引入到支持向量机识别优化模型的求解中，对人工蜜蜂群从搜索策略、蜜源编码、更新策略等方面进行了改进。通过仿真试验获取水电机组的四类典型轴心轨迹样本，对轴心轨迹中提取的19种特征参数和支持向量机参数进行了同步优化，将改进HABC算法与PSO-SVM算法和GA-SVM算法进行了对比。研究结果表明HABC—SVM具有良好的自适应性和分类精度，可以同步获取支持向量机参数和特征子集的最优解，增强分类器的性能，提高轴心轨迹模式识别的准确率，对水电机组的故障诊断工程应用有一定的指导意义。

英文摘要：

In the research of identification of shaft orbit of hydropower generating unit, the selection of feature parameter in traditional SVM system is not adaptive, which results in lower classification performance and long computation time. Aiming at the problems above, this paper proposes a novel method to identify the shaft orbit based on HABC-SVM. Artificial bee colony is introduced to the solution of SVM identification optimal model, and the search strategy, food source and update equation of artificial bee swarm are improved. Through the simulation experiment, four typical samples of shaft orbit of hydraulic turbines are obtained, the 19 kinds of feature parameters extracted from shaft orbit and parameters of SVM are optimized synchronously, and the improved HABC algorithm is compared with PSO-SVM algorithm and GA-SVM algorithm. The results show that HABC-SVM has good adaptability and classification accuracy, can acquire the optimal solutions of SVM parameters and feature subset synchronously, enhance the performance of classifier, and improve the precision of identification of shaft orbit, which has some guidance significance to fault diagnosis of hydropower generating unit. This work is supported by National Natural Science Foundation of China （No. 51079057, No. 51039005 and No. 51109088）.