摘要
针对滚动轴承早期故障特征难以准确提取的问题,提出了一种基于自适应变分模态分解(adaptive variational mode decomposition,AVMD)的早期故障诊断方法。该方法建立了一种无需先验知识的故障冲击度量指标(fault impact measure index,FIMI),以指导多策略改进的鹦鹉算法(improved parrot optimizer,IPO)自适应获得变分模态分解(variational mode decomposition,VMD)的最优参数组合[K,α],实现故障信号的精准分解;基于FIMI最大化准则提取主故障特征模态分量;对其进行增强包络谱分析,从而识别故障类型。仿真信号和试验数据证实了该方法在滚动轴承早期故障诊断方面的有效性,并展示了其相对于现有技术方法的优越性。
Aiming at the problem that it is difficult to accurately extract the incipient fault features of rolling bearings,an incipient fault diagnosis method based on adaptive variational mode decomposition(AVMD)is proposed.A new fault impact measure index(FIMI)is established to guide the multi-strategy improved parrot optimizer(IPO)to adaptively obtain the optimal parameter combination[K,α]of variational mode decomposition(VMD),so as to realize the accurate decomposition of fault signal.The principal fault characteristic component is extracted based on the FIMI maximization criterion.The fault component undergoes enhanced envelope spectrum processing to identify the fault type.Numerical simulations and experimental data confirm the method’s effectiveness and feasibility for incipient fault diagnosis of rolling bearings,showcasing its superiority over existing techniques.
作者
陆志杰
张子恒
马晨波
张玉言
王志良
LU Zhijie;ZHANG Ziheng;MA Chenbo;ZHANG Yuyan;WANG Zhiliang(College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China;CSC Bearing Co.,Ltd.,Changshu 215500,China)
出处
《振动工程学报》
北大核心
2025年第12期3101-3112,共12页
Journal of Vibration Engineering
基金
江苏省成果转化项目(BA2023050)。
关键词
滚动轴承
早期故障
自适应变分模态分解
故障冲击度量指标
鹦鹉算法
rolling bearings
incipient fault
adaptive variational mode decomposition
fault-impact measure index
parrot optimizer
