齿轮裂纹等级的识别对于齿轮箱故障诊断具有重要意义.通过随机化邻域约简,生成一系列邻域可分子空间,从而形成不同的子分类器.通过最小化间隔损失或者求解L1正则最小平方损失问题来改变间隔分布,从而得到子分类器权值,对分类器按权值排序后,选择使得训练集分类精度最高的子分类器集合.实验结果表明,对于齿轮裂纹等级的识别,该方法的性能大大优于现有的其它方法.%Gear crack level identification is of great significance for gear box fault diagnosis. Regarding instability and performance limitation in current identification, we generated a set of neighborhood separable subspaces based on randomized attribute reduction, on which a set of base classifiers were obtained. The weight vector of the base classifiers was learned by optimizing loss of ensemble margin and regularization learning to change margin distribution. Base classifiers were ranked according to the weight value and a set of classifiers that make the ensemble classification accuracy highest on the training set was got. The experiment analysis shows that the proposed method is much better than other state of the art methods in crack level identification.
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