Dual-rotor misalignment fault quantitative identification based on DBN and improved D-S evidence theory

被引：9

作者：

Yang Dalian ^{[1
]}

Zhang Fanyu ^{[1
]}

Miao Jingling ^{[1
]}

Zhang Hongxian ^{[1
,3
]}

Li Renjie ^{[1
]}

Tao Jie ^{[2
]}

机构：

[1] Hunan Univ Sci & Technol, Hunan Prov Key Lab Hlth Maintenance Mech Equipmen, Xiangtan 411201, Peoples R China

[2] Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411201, Peoples R China

[3] Guangxi Univ Sci & Technol, Lushan Coll, Liuzhou 545006, Peoples R China

来源：

MECHANICS & INDUSTRY | 2021年 / 22卷

基金：

中国国家自然科学基金;

关键词：

Deep belief network; mutual information measure; D-S evidence theory; dual-rotor system; misalignment fault quantitative identification;

D O I：

10.1051/meca/2021022

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Misalignment fault is the main factor that affects the normal running of dual-rotor system. Quantitative identification the misalignment fault is an important way to ensure the safe and stable service of the dual-rotor system, while the identification accuracy of traditional methods is low. Aiming at the above problems, this paper proposed a dual-rotor misalignment fault quantitative identification method based on DBN and D-S evidence theory improved by mutual information measure (MIMD-S). Seven groups experiments were conducted and several vibration signals were collected. By comparing it with the traditional methods D-S, and Pignistic improved D-S (PD-S) evidence theory, the results show that the method proposed in this paper improves the accuracy of the misalignment fault quantitative identification of the dual-rotor, the identification error rate was only 0.36%.

引用

页数：10

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