基于Fisher Score与最大信息系数的齿轮箱故障特征选择方法

Fault feature selection method of gearbox based on Fisher Score and maximum information coefficient

针对工业环境中齿轮箱多故障特征难以选择的问题,结合Fisher Score与最大信息系数(MIC)构建一种新的故障特征优化选择方法.首先,考虑到多故障特征分布不均匀和重叠性问题,采用Fisher Score计算方法构建特征指标重要度排序规则;其次,在考虑冗余特征对有效特征表征的影响基础上,利用最大信息系数构建特征间关联性评价方法,对冗余特征实现更新排序;再次,以分类准确率为判断依据,基于支持向量机理论(SVM)对排序模型进行修正,建立基于Fisher Score与最大信息系数的故障特征优化选择方法;最后,利用UCI标准数据集和实验仿真的齿轮箱故障数据进行实验以验证所提出算法的有效性和工程实用性.仿真实验对比分析表明,与传统的mRMR、reliefF方法相比,所提出的方法特征子集数量适中,准确率更高.

Aiming at the problem that it is difficult to select multiple fault features of gearboxes in industrial environment,a new fault feature optimization selection method combining Fisher Score and maximum information coefficient(MIC)is proposed.First,considering about uneven distribution and overlapping of multi-fault features,the Fisher Score calculation method is used to construct the ranking rules of the importance of the feature indicators.Second,based on the impact of redundant features on the effective feature representation,the maximum information coefficient is used to update and rank redundant features.Then,taking classification accuracy as the judgement basis,using the support vector machine(SVM)theory,a fault feature optimization selection method combining Fisher Score and maximum information coefficient is established.Finally,the UCI standard data set and the gear failure simulation data set are used to verify the effectiveness and engineering practicability of the proposed algorithm.Comparative analysis of simulation experiments shows that compared with the traditional mRMR and reliefF methods,the number of feature subsets proposed is moderate and the accuracy is higher.