首页|变分模态分解方法在轴承故障诊断中的应用研究进展

变分模态分解方法在轴承故障诊断中的应用研究进展

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作为旋转机械的核心和易发生故障的部件,滚动轴承及其故障诊断是目前研究的热点和前沿.有效的特征提取方法对于滚动轴承故障诊断至关重要,其中变分模态分解算法(VMD)因对复杂信号具有较强的分析能力和自适应性,应用潜力较好.对VMD的基本原理及其优势、VMD在轴承故障特征提取方面的应用、VMD参数优化方法以及最新进展进行归纳总结,针对VMD参数优化问题,从适应度函数构造和群智能算法改进上,提出一种新的解决方法,并探讨VMD在诊断滚动轴承早期微弱故障和复合故障等方面的不足之处,最后从理论研究和工程应用的角度,展望VMD未来的发展方向,可为从事滚动轴承故障诊断的相关研究人员提供参考.
Research Progress in Application of Variational Mode Decomposition Method in Bearing Fault Diagnosis
As the core and fault prone part of rotating machinery,rolling bearing and its fault diagnosis are the focus and frontier of current research.The effective feature extraction method is very important for the fault diagnosis of rolling bear-ing,and the variational mode decomposition algorithm(VMD)has a good application potential because of its strong analy-sis ability and adaptability to complex signals.The basic principle and advantages of VMD,the application of VMD in bear-ing fault feature extraction,VMD parameter optimization methods and the latest progress were summarized.A new solution to the VMD parameter optimization problem was proposed from the aspects of fitness function construction and group intel-ligence algorithm improvement.The shortcomings of VMD in diagnosing early weak fault and compound fault of rolling bear-ing were discussed.The future development direction of VMD was prospected from the perspective of theoretical research and engineering application,which can provide reference for relevant researchers in fault diagnosis of rolling bearing.

rolling bearingfault diagnosisfeature extractionvariational mode decompositionoptimization of parameters

陆志杰、王志良、鄢小安、刘德利、孙见君、马晨波

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南京林业大学机械电子工程学院,江苏南京 210037

苏州长城精工科技股份有限公司,江苏常熟 215500

滚动轴承 故障诊断 特征提取 变分模态分解算法 参数优化

国家自然科学基金项目

52005265

2024

润滑与密封
中国机械工程学会 广州机械科学研究院有限公司

润滑与密封

CSTPCD北大核心
影响因子:0.478
ISSN:0254-0150
年,卷(期):2024.49(9)