Du Wenhua, Guo Xiaoming, Wang Zhijian, Wang Junyuan, Yu Mingrang, Li Chuanjiang, Wang Guanjun, Wang Longjuan, Guo Huaichao, Zhou Jinjie, Shao Yanjun, Xue Huiling, Yao Xingyan
College of Mechanical Engineering, North University of China, Taiyuan 030051, China.
School of Mechanical and Electrical Engineering, North University of China, Taiyuan 030051, China.
Entropy (Basel). 2019 Dec 24;22(1):27. doi: 10.3390/e22010027.
The self-organizing fuzzy (SOF) logic classifier is an efficient and non-parametric classifier. Its classification process is divided into an offline training stage, an online training stage, and a testing stage. Representative samples of different categories are obtained through the first two stages, and these representative samples are called prototypes. However, in the testing stage, the classification of testing samples is completely dependent on the prototype with the maximum similarity, without considering the influence of other prototypes on the classification decision of testing samples. Aiming at the testing stage, this paper proposed a new SOF classifier based on the harmonic mean difference (HMDSOF). In the testing stage of HMDSOF, firstly, each prototype was sorted in descending order according to the similarity between each prototype in the same category and the testing sample. Secondly, multiple local mean vectors of the prototypes after sorting were calculated. Finally, the testing sample was classified into the category with the smallest harmonic mean difference. Based on the above new method, in this paper, the multiscale permutation entropy (MPE) was used to extract fault features, linear discriminant analysis (LDA) was used to reduce the dimension of fault features, and the proposed HMDSOF was further used to classify the features. At the end of this paper, the proposed fault diagnosis method was applied to the diagnosis examples of two groups of different rolling bearings. The results verify the superiority and generalization of the proposed fault diagnosis method.
自组织模糊（SOF）逻辑分类器是一种高效的非参数分类器。其分类过程分为离线训练阶段、在线训练阶段和测试阶段。通过前两个阶段获得不同类别的代表性样本，这些代表性样本称为原型。然而，在测试阶段，测试样本的分类完全依赖于相似度最大的原型，而没有考虑其他原型对测试样本分类决策的影响。针对测试阶段，本文提出了一种基于调和平均差的新型SOF分类器（HMDSOF）。在HMDSOF的测试阶段，首先，根据同一类别中每个原型与测试样本之间的相似度对每个原型进行降序排序。其次，计算排序后原型的多个局部均值向量。最后，将测试样本分类到调和平均差最小的类别中。基于上述新方法，本文采用多尺度排列熵（MPE）提取故障特征，采用线性判别分析（LDA）对故障特征进行降维，并进一步使用所提出的HMDSOF对特征进行分类。在本文结尾，将所提出的故障诊断方法应用于两组不同滚动轴承的诊断实例。结果验证了所提出故障诊断方法的优越性和通用性。
