摘要
针对滚动轴承的复合故障诊断问题,深入研究了一种基于优化特征模态分解和快速谱相关的复合故障诊断方法。首先,通过理论分析,提出脉冲能量因子指标来实现特征模态分解的参数选择以及最优分量的选取;然后,基于快速谱相关原理设计谱相关相对强度曲线和改进快速谱相关图,用于确定不同故障调制后对应的最优载波,对最优载波进行包络处理,从而分离轴承的复合故障特征,最终实现复合故障的准确性诊断。通过模拟故障试验和工程案例分析结果表明,该文所提方法相比于经验模态分解能够有效滤除噪声干扰,具有良好的鲁棒性,同时,避免了解卷积方法设定参数的缺陷,且与Autogram方法相比,能够有效分离复合故障特征,避免复合故障特征成分耦合。
Here,aiming at the problem of composite fault diagnosis of rolling bearing,a composite fault diagnosis method based on optimized feature mode decomposition and fast spectral correlation was deeply studied.Firstly,through theoretical analysis,the pulse energy factor index was proposed to realize parametric selection and optimal component selection for feature mode decomposition.Then,based on the principle of fast spectral correlation,a spectral correlation relative strength curve and an improved fast spectral correlation graph were designed to determine the optimal carrier wave corresponding to different fault modulations.The envelope processing of the optimal carrier wave was performed to separate composite fault features of bearing,and ultimately realize correct diagnosis of composite faults.Through simulation fault tests and engineering case analysis,the results showed that compared to EMD,the proposed method can effectively filter out noise interference and has good robustness;at the same time,it avoids defects of parametric setting for deconvolution method;compared to Autogram method,it can effectively separate composite fault features and avoid coupling of composite fault feature components.
作者
唐贵基
张龙
薛贵
徐振丽
王晓龙
TANG Guiji;ZHANG Long;XUE Gui;XU Zhenli;WANG Xiaolong(Department of Mechanical Engineering,North China Electric Power University,Baoding 071003,China;Hebei Provincial Key Lab of Health Maintenance and Failure Prevention of Electric Machinery Equipment,North China Electric Power University,Baoding 071003,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2024年第15期160-168,共9页
Journal of Vibration and Shock
基金
国家自然科学基金资助项目(52005180)
河北省自然科学基金资助项目(E2022502003)
中央高校基本科研业务费专项资金资助项目(2023MS127)
南太湖精英计划创新人才团队项目。
关键词
滚动轴承
复合故障
特征分离
特征模态分解
快速谱相关
rolling bearing
composite fault
feature separation
feature mode decomposition
fast spectral correlation
