Research on Fault Diagnosis of Mine Ventilator Bearing Based on Cross Fintropy Algorithm

被引：2

作者：

Bian, Li ^{[1
]}

Sun, Hongna ^{[2
]}

He, Hui ^{[3
]}

Liu, Chengyang ^{[3
]}

Guan, Zhongzhi ^{[4
]}

机构：

[1] Guangdong Ocean Univ, Sch Elect Informat Engn, Zhanjiang 524088, Peoples R China

[2] Heilongjiang Univ Sci & Technol, Sch Elect & Elect Informat Engn, Harbin 150022, Peoples R China

[3] Heilongjiang Univ Sci & Technol, Sch Elect & Control Engn, Harbin 150022, Peoples R China

[4] Heilongjiang Univ Sci & Technol, Sch Architecture & Civil Engn, Harhin 150022, Peoples R China

来源：

2020 35TH YOUTH ACADEMIC ANNUAL CONFERENCE OF CHINESE ASSOCIATION OF AUTOMATION (YAC) | 2020年

关键词：

Terms Cross entropy algorithm; Bearing fault diagnosis; Support vector machine; Rough set;

D O I：

10.1109/YAC51587.2020.9337697

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In the construction and production of coal mines, the mine fan is obviously very important, and its function is to ensure the safety of the underground workers in the mine. If the mine fan fails, it will cause inestimable losses and bring subsequent problems. Therefore, it is necessary to study the safe use and operation of mine ventilator. Aiming at the common bearing failures of mine ventilators, this paper innovates a fault diagnosis model based on rough set attribute reduction and cross entropy algorithm. Through the study of the model, the following conclusions are drawn: This paper combines rough set attribute reduction and cross entropy algorithm, which is very good for fault detection of mine fan bearings, and can be considered in actual production.

引用

页码：407 / 411

页数：5

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