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Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network

文献类型：期刊论文

作者Chen, Zenghua1; Zhu, Lingjian2; Lu, He3; Chen, Shichao1,4; Zhu, Fenghua4,5; Liu, Sheng1,4; Han, Yunjun1,4; Xiong, Gang1,4,5
刊名SCIENTIFIC REPORTS
出版日期2024-07-05
卷号14期号:1页码:19
关键词Rolling bearings Fault diagnosis Genetic algorithm BP neural network Optimization
ISSN号2045-2322
DOI10.1038/s41598-024-66318-0
通讯作者Xiong, Gang(gang.xiong@ia.ac.cn)
英文摘要Health monitoring and fault diagnosis of rolling bearings are crucial for the continuous and effective operation of mechanical equipment. In order to improve the accuracy of BP neural network in fault diagnosis of rolling bearings, a feature model is established from the vibration signals of rolling bearings, and an improved genetic algorithm is used to optimize the initial weights, biases, and hyperparameters of the BP neural network. This overcomes the shortcomings of BP neural network, such as being prone to local minima, slow convergence speed, and sample dependence. The improved genetic algorithm fully considers the degree of concentration and dispersion of population fitness in genetic algorithms, and adaptively adjusts the crossover and mutation probabilities of genetic algorithms in a non-linear manner. At the same time, in order to accelerate the optimization efficiency of the selection operator, the elite retention strategy is combined with the hierarchical proportional selection operation. Using the rolling bearing dataset from Case Western Reserve University in the United States as experimental data, the proposed algorithm was used for simulation and prediction. The experimental results show that compared with the other seven models, the proposed IGA-BPNN exhibit superior performance in both convergence speed and predictive performance.
资助项目National Key Research and Development Program of China[2023YFF0612702] ; Key R&D Projects of Shaanxi Province[2020ZDLGY10-04] ; Science and Technology Project of Guangdong Quality Improvement and Development[2021ZJ04] ; Jiangxi Provincial Natural Science Foundation[20232ABC03A07] ; Key Research and Development Program of Rizhao[2023ZDYF010153]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:001263443800001
出版者NATURE PORTFOLIO
资助机构National Key Research and Development Program of China ; Key R&D Projects of Shaanxi Province ; Science and Technology Project of Guangdong Quality Improvement and Development ; Jiangxi Provincial Natural Science Foundation ; Key Research and Development Program of Rizhao
源URL[http://ir.ia.ac.cn/handle/173211/59260]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Xiong, Gang
作者单位1.Chinese Acad Sci, Inst Automat, State Key Lab Multimodal Artificial Intelligence S, Beijing 100190, Peoples R China
2.Xian Univ Technol, Sch Mech & Precis Instrument Engn, Xian 710048, Peoples R China
3.Renmin Univ China, Sch Educ, Beijing 100872, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing Engn Res Ctr Intelligent Syst & Technol, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Guangdong Engn Res Ctr Printing & Intelligent Mfg, Cloud Comp Ctr, Dongguan 523808, Peoples R China
推荐引用方式
GB/T 7714		 Chen, Zenghua,Zhu, Lingjian,Lu, He,et al. Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network[J]. SCIENTIFIC REPORTS,2024,14(1):19.
APA Chen, Zenghua.,Zhu, Lingjian.,Lu, He.,Chen, Shichao.,Zhu, Fenghua.,...&Xiong, Gang.(2024).Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network.SCIENTIFIC REPORTS,14(1),19.
MLA Chen, Zenghua,et al."Research on bearing fault diagnosis based on improved genetic algorithm and BP neural network".SCIENTIFIC REPORTS 14.1(2024):19.

入库方式： OAI收割

来源：自动化研究所

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