瞬态成分参数的最小二乘法辨识及其轴承故障特征提取应用

LSM-based Transient Parameter Identification and Its Application in Feature Extraction of Bearing Fault

机械系统中轴承出现剥落、裂纹等局部故障,运行时振动信号中出现瞬态冲击响应成分,可通过瞬态成分的检测与提取实现故障特征提取。在瞬态成分建模的基础上,提出基于最小二乘法的瞬态成分参数辨识方法,并将其应用于轴承局部故障时振动信号中瞬态成分特征迭代提取。基于Morlet小波参数化表达式建立双边不对称的瞬态成分模型,应用Levenbery-Marquardt方法辨识模型参数,迭代提取信号中的瞬态成分,并通过Wigner-Ville分布获得瞬态成分高聚集性且瞬态成分之间无交叉项的故障特征时频表示。将基于最小二乘法的瞬态成分参数辨识方法应用于轴承局部故障特征提取,结果表明:该方法能通过参数辨识提取各瞬态成分,瞬态成分时频分布将故障的时频特征以高聚集性且瞬态成分之间无交叉项的形式表示出来,从而有效提取轴承故障特征。

Localized faults,such as spalling and crack,in rotating machinery parts tend to result in shocks and thus arouse transient impulse responses in the vibration signal and thus present a potential approach for fault feature extraction.Based on transient modeling,a method combining with least square method is proposed and applied to iteratively identify transient parameters.Based on Morlet wavelet parametric expression,a double-side asymmetric transient model is firstly built;then,Levenbery-Marquardt method is introduced to identify parameters of the model.With the transients extracted from the signal,Wigner-Ville distribution is applied to show high resolution and no cross item time-frequency representation of transients.The transient parameter identification method based on LSM is used to extract feature of a faulted bearing,and the results show that the transients is obtained through the proposed method and eventually time-frequency feature of the fault is well expressed in a high resolution and no cross item form.