Abstract
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.
Highlights
A New Fuzzy Logic Classifier Based on MultiscaleWenhua Du 1 , Xiaoming Guo 1 , Zhijian Wang 1, *, Junyuan Wang 1 , Mingrang Yu 2 , Chuanjiang Li 3 , Guanjun Wang 4,5, *, Longjuan Wang 4,5 , Huaichao Guo 6 , Jinjie Zhou 1 , Yanjun Shao 1 , Huiling Xue 1 and Xingyan Yao 7
Rotating machinery has been widely used in various modern industries such as wind turbines, aero engines, water turbines, and gas turbines
In order to improve the classification accuracy, in this paper, we propose a self-organizing fuzzy (SOF) classifier based on harmonic mean difference, which is called HMDSOF
Summary
Wenhua Du 1 , Xiaoming Guo 1 , Zhijian Wang 1, *, Junyuan Wang 1 , Mingrang Yu 2 , Chuanjiang Li 3 , Guanjun Wang 4,5, *, Longjuan Wang 4,5 , Huaichao Guo 6 , Jinjie Zhou 1 , Yanjun Shao 1 , Huiling Xue 1 and Xingyan Yao 7. Collage of Information and Communication Engineering, Hainan University, Haikou 570228, China. Received: 11 November 2019; Accepted: 19 December 2019; Published: 24 December 2019
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