Dimension Reduction Method of Rotor Fault Dataset Based on MGCD

被引：0
|
作者：
Zhang Y. [1 ]
Zhao R. [1 ]
Deng L. [1 ]
机构：
[1] School of Mechanical and Electrical Engineering, Lanzhou University of Technology, Lanzhou
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2024年 / 44卷 / 02期
关键词：
dimensionality reduction; far neighbor graph; fault diagnosis; small sample;
D O I：
10.16450/j.cnki.issn.1004-6801.2024.02.009
中图分类号：
学科分类号：
摘要：
A rotor fault dataset dimensionality reduction algorithm based on multi graph collaborative decision making (MGCD) is proposed, in order to address the issues of difficulty in classifying fault datasets and low accuracy in fault pattern recognition due to high feature dimensions. This algorithm first builds on the framework of marginal Fisher analysis (MFA) algorithm to solve the problem of local inseparability of fault categories caused by a single graph structure, through establishing nearest neighbor graphs and far neighbor graphs. Secondly, it uses the maximum divergence weighted difference method to try to weaken the impact of small sample problems. The performance of the algorithm is verified using two different structural types of rotor system fault simulation datasets. The results show that the sensitive fault dataset obtained by using this algorithm to reduce the dimensionality of the fault dataset, can make the differences between fault categories more prominent, thereby improving the accuracy of fault pattern recognition. This study can provide a certain research reference for improving the level of intelligent fault diagnosis technology in rotating machinery. © 2024 Nanjing University of Aeronautics an Astronautics. All rights reserved.
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页码：266 / 273
页数：7
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