基于参数自适应特征模态分解的滚动轴承故障诊断方法

A rolling bearing fault diagnosis method based on parameter-adaptive feature mode decomposition

针对强背景噪声下轴承故障信息难以有效提取的问题,提出一种基于参数自适应特征模态分解的滚动轴承故障诊断方法。首先,为了克服原始特征模态分解(FMD)需要依赖人为经验设定关键参数而不具有自适应性的缺点,提出基于平方包络谱特征能量比(FER-SES)的网格搜索方法自动地确定FMD的模态个数n和滤波器长度L;随后,采用参数优化的FMD将原轴承振动信号划分为n个模态分量,并选取具有最大FER-SES的模态分量为敏感模态分量;最后,通过计算敏感模态分量的平方包络谱来提取故障特征频率,从而判别轴承故障类型。通过仿真信号和工程案例分析验证了提出方法的有效性。与变分模态分解(VMD)和谱峭度方法(SK)相比,提出方法具有更好的故障特征提取性能。

The bearing fault signatures are difficult to be extracted effectively under strong background noises.To address this issue,this article proposes a rolling bearing fault diagnosis method based on the parameter adaptive feature mode decomposition.Firstly,to overcome the shortcoming that the original characteristic mode decomposition(FMD)needs to rely on human experience to set its key parameters without adaptability,the grid search method based on feature energy ratio of squared envelope spectrum(FER-SES)is presented to automatically determine the mode number n and the filter length L of FMD.Then,the original bearing vibration signals are divided into n mode components by parameter optimized FMD.The mode component with the maximum FER-SES is selected as the sensitive mode component.Finally,the fault characteristic frequency is extracted by calculating the squared envelope spectrum of sensitive mode component to distinguish bearing fault types.The effectiveness of the proposed method is evaluated by simulation signal and engineering case analysis.Compared with variational mode decomposition and spectral kurtosis,the proposed method has better fault feature extraction performance.