Rolling Bearing Fault Diagnosis of SVM Based on Improved Quantum Genetic Algorithm

被引：0
|
作者：
Xu D. [1 ]
Ge J. [1 ]
Wang Y. [1 ]
Wei F. [2 ]
Shao J. [1 ]
机构：
[1] School of Mechanical and Dynamic Engineering, Harbin University of Science and Technology, Harbin
[2] School of Mechatronics Engineering, Harbin Institute of Technology, Harbin
来源：
Zhendong Ceshi Yu Zhenduan/Journal of Vibration, Measurement and Diagnosis | 2018年 / 38卷 / 04期
关键词：
Characteristic sensitivity; Hybrid feature evaluation; Quantum genetic algorithm; Rolling bearing fault diagnosis; Support vector machine;
D O I：
10.16450/j.cnki.issn.1004-6801.2018.04.028
中图分类号：
学科分类号：
摘要：
There is an "under-learning" problem in the feature sensitivity of feature evaluation method for a single measure model. The support vector machine (SVM) parameter optimization algorithm has the disadvantages of slow convergence rate and easy to fall into the local extreme. Rolling bearing fault diagnosis of SVM based on improved quantum genetic algorithm method is proposed. Firstly, the characteristics of time domain, frequency domain constitute multi-domain original fault feature set. Secondly, a weighted model feature evaluation model based on correlation, distance and information is constructed. Finally, the weighted fault feature set is taken as input, and the quantum entropy is introduced into the improved quantum genetic algorithm (IQGA) to optimize the structural parameters of SVM. The intelligent identification of rolling bearing failure mode is completed. The experimental results show that compared with the classical quantum genetic algorithm (CQGA) and genetic algorithm (GA), the proposed method can quickly converge to the global optimal solution and ensure the clustering performance, and improve the diagnostic accuracy of rolling bearing. © 2018, Editorial Department of JVMD. All right reserved.
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页码：843 / 851
页数：8
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