摘要
针对滚动轴承的故障特征信息一般较为微弱且难以提取的问题,提出了自适应VMD故障特征提取方法。将最大峭度指标和最小包络熵组合成复合指标,并结合粒子群优化算法对VMD参数进行优化;应用优化参数后的VMD算法对待测信号进行分解,根据最大峭度指标选取最佳模态分量;对最佳模态分量进行Hilbert算法的包络解调处理,从包络谱中提取出故障特征信息。仿真和实验结果验证了该方法在滚动轴承故障诊断方面的可行性。
Aiming at the problem that the fault feature information of rolling bearing is weak and difficult to extract,an adaptive VMD fault feature extraction method was proposed.The maximum kurtosis index and the minimum envelope entropy were combined into the composite index,and the VMD parameters were optimized by the particle swarm optimization algorithm.The VMD algorithm after optimizing parameters was used to decompose the measured signal,and the optimal modal component was selected according to the maximum kurtosis index.The optimal modal component was demodulated by Hilbert algorithm to extract fault feature information from the envelope spectrum.Simulation and experimental results show that the method is feasible in fault diagnosis of rolling bearing.
作者
王杰
郭世伟
Wang Jie;Guo Shiwei(School of Mechanical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《机电工程技术》
2020年第11期161-164,共4页
Mechanical & Electrical Engineering Technology
基金
国家自然科学基金项目(编号:51875481)。
关键词
滚动轴承
故障诊断
变分模态分解
粒子群优化算法
rolling bearing
fault diagnosis
variational modal decomposition
particle swarm optimization algorithm
