LSM-based transient parameter identification and its application in feature extraction of bearing fault

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.