基于模态筛选的滚动轴承微小故障诊断研究

Research on Incipient Fault Diagnosis of Rolling Bearing Based on Mode Selection

滚动轴承的自转、部件相互作用和强干扰使测量的振动信号复杂,而滚动轴承的早期损伤类故障特征信息弱小,导致故障特征难以有效提取从而影响故障诊断,因此提出一种敏感模态筛选方法来提高故障诊断的正确性。采用变分模态分解算法对信号进行自适应分解,提取信号的局部特征。基于排列熵的不变性和对冲击信号的敏感性,提出故障因子的敏感模态筛选方法对信号进行重构,抑制振动信号中的干扰和噪声。通过仿真和实验对所提方法进行了验证,结果表明,相比其他处理方法,所提方法信噪比提高了60.5%,误差降低了52.6%,同时故障诊断正确率提高了24.3%,说明了该方法的有效性。

The vibration signals measured from a rolling bearing are complicated because of bearing rotation,component interac⁃tion and strong interference.It is difficult to extract fault features effectively for incipient fault because the features for incipient fault are small.The accuracy of fault diagnosis will be decreased.Therefore,a sensitive mode selection method was proposed to im⁃prove the accuracy of fault diagnosis.Firstly,Variational Mode Decomposition(VMD)algorithm was used to adaptively decom⁃pose the vibration signal and extract the local signal features.Secondly,based on the invariance of permutation entropy and its sen⁃sitivity to impulse signal,a sensitive mode selection method of fault factors was proposed to reconstruct the signal.The method could suppress the interference and noise in the vibration signals.Finally,comparison with other preprocessing methods using simulation and experiment has also been investigated.The simulation results show that the signal-to-noise ratio is improved by 60.5%and the root mean square error is reduced by 52.6%.The experimental results show the accuracy of fault diagnosis is in⁃creased by 24.3%.These results have confirmed that the fault factor method provides an effective method for fault diagnosis of roll⁃ing bearings.