Generator Fault Classification Method Based on Multi-Source Information Fusion Naive Bayes Classification Algorithm

Yi Wang, Yuhao Huang, Kai Yang, Zhihan Chen and Cheng Luo
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Yi Wang: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Yuhao Huang: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Kai Yang: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zhihan Chen: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Cheng Luo: School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China

Energies, 2022, vol. 15, issue 24, 1-10

Abstract: The existing motor fault classification methods mostly use sensors to detect a single fault feature, which makes it difficult to ensure high diagnostic accuracy. In this paper, a motor fault classification method based on multi-source information fusion Naive Bayes classification algorithm is proposed. Firstly, this paper introduces the concept and advantages of multi-source information fusion, as well as its problems of miscellaneous information and inconsistent data magnitude. For example, as this paper classifies the fault of generators, there are many physical quantities, such as voltage, current and temperature, which are not in the same dimension, therefore it is difficult to fuse. Then, aiming at the corresponding problems, this paper uses a PCA dimension reduction method to remove redundant information and reduce the dimension of multi-dimensional complex information. Aiming at the problem of unequal data magnitude, the interval mapping method is adopted to effectively solve the misjudgment caused by unequal data magnitude. After the initial multi-source information processing, the classical Naive Bayes classification algorithm is used for fault classification, and the algorithm diagnosis and verification are carried out according to the statistical fault data. Use of the algorithm increases accuracy to more than 97%.

Keywords: multi-source information fusion; finite element analysis; Naive Bayes classification algorithm; fault classification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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