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中文摘要：
水电机组振动故障诊断中常面临样本稀缺及分布不均匀、不平衡等问题,严重影响诊断结果。针对此类问题提出一种基于模糊K近邻（K nearest neighbor,KNN）支持向量数据描述（support vector data description,SVDD）的故障诊断模型。首先利用核变换将故障样本映射到高维特征空间,并采用SVDD提取不平衡故障样本域的边界支持向量样本,构建基于相对距离模糊阈值和KNN的决策规则,最终在此基础上建立机组故障诊断模型。用该模型对经过不平衡处理的国际标准测试数据样本进行测试实验,并与支持向量机（support vector machine,SVM）及目前应用较多的SVDD模型的分类结果进行对比,结果表明该模型可有效解决不平衡样本分类倾斜性问题。最后,将模型用于某水电厂机组振动故障诊断,取得了较高的诊断精度,证明了该方法的有效性。
英文摘要：
The fault samples of hydro-electric generating unit have always been unevenly or imbalanced distributed,which seriously affects the classification accuracy.In order to overcome this disadvantage,a novel support vector data description（SVDD） algorithm improved with fuzzy K nearest neighbor（KNN） decision was proposed.Firstly,the samples were mapped to high-dimensional feature space with kernel transformation,and SVDD was used to extract support vector samples.Then decision rules based on fuzzy threshold and KNN were determined and the novel SVDD algorithm was realized with the new rules.In order to assess the performance of the novel algorithm,two previous SVDD models based on different decisions and support vector machine（SVM） were applied for the comparison with imbalanced datasets from University of California Irvine（UCI） Machine Learning Repository.The experimental results show that the algorithm proposed can efficiently improve the accuracy of classification for imbalanced and uneven samples.At last,the successful application in the fault diagnosis for hydro-electric generating unit attests the effectiveness of the proposed model.
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