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中文摘要：
为了提高复杂机械故障诊断的确诊率，提出了一种基于改进人工蜂群算法（improved artifi—cial bee colony，IABC）优化LSSVM多分类器组的故障诊断模型。该模型利用多特征提取方法，获取了较为完备的时频域特征信息，同时选择具有较强搜索能力和快速收敛性的IABC算法优化了LSSVM分类器的参数，提高了分类效率，在诊断决策层，利用评估矩阵进行了多分类器诊断结果的融合决策。通过与传统方法的对比表明：该诊断模型不仅能获取完备的故障特征信息，而且能更快地荻取LSSVM最优分类参数；同时，基于评估矩阵的融合决策能够充分考虑各子分类器的性能差异，保证了诊断决策的高效精确。多种数据仿真表明，该诊断模型适用于机械故障诊断。
英文摘要：
In order to improve the fault diagnosis accuracy,the paper presented a fault diagnosis model based on IABC optimized multiple LSSVM classifier group. It used multiple feature extraction methods to get the complete time--frequency domain features. Then IABC was utilized to optimize LSSVM parameters,because it had strong searching ability and fast convergence to improve classifica- tion efficiency. And in the diagnostic decisions stage, the criterion matrix was utilized to make the deci- sion of the multiple classifiers. Through the comparison with the traditional methods, the results show that it can obtain complete features information,and can get LSSVM parameters more quickly and ef- fectively. The fusion decision based on criterion matrix fully considering the differences of sub--classi- fiers performance, and it ensures the higher accuracy of diagnostic decisions. Finally, the various simu- lation results show that the algorithm can be used in mechanical fault diagnosis well.
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