基于云理论和Relief⁃F的滚动轴承故障识别方法

Fault Classification Method of Rolling Bearing Based on Cloud Theory and Relief⁃F

为了充分利用海量数据中蕴含的信息并对轴承故障进行有效识别,采用云理论方法将轴承的故障数据与其对应的故障类型进行映射,建立了滚动轴承在不同状态下各个特征的云分布模型,并依此构造出轴承故障的云判断知识库。同时,引入Relief⁃F算法确定训练集各特征的权重系数,结合云分布隶属度系数,提出了样本对于轴承故障的最终隶属度计算方法。通过根据不同数目的训练样本建立的云分类知识库在分类精度上的对比,证明了该方法具备对数据的学习能力。将该分类方法与常用的分类方法在含有噪声的测试样本上进行对比实验,证明了该分类方法在抗噪性方面的优越性。

In order to make full use of the information contained in the massive data and effectively identify the bearing faults,the cloud theory method is used to map the bearing fault data to its corresponding fault type,and the cloud distribution model of each feature of the rolling bearing under different states is established.Based on this,a cloud judgment knowledge base of bearing faults is constructed.Meanwhile,the Relief-F method is introduced to determine the weight coefficients of each feature of the training set.Combined with the cloud distribution membership coefficient,the final membership calculation method for bearing faults is proposed.Through the comparison of classification accuracy between cloud classification knowledge bases established by different numbers of training samples,it is proved that the method has the ability to learn data.Moreover,the classification method and other traditional classification methods are tested by using noise-containing test samples,and the results show the superiority of the classification method in terms of noise resistance.