Vibration shock disturbance modeling in the rotating machinery fault diagnosis: A generalized mixture Gaussian model

In real-world industrial environments, complex background noises are composed of various components, deviating from a simple Gaussian distribution like shock noise. In this work, a robust noise modeling method based on the mixture of exponential power (MoEP) distributions is developed to address this issue. To proficiently extract the fault characteristics, the signal's 2-D representation is attained via Fast-SC, both of the fault features’ low-rankness and the complex noise are combined in a signal model. Then, a penalized function of the noise model is combined to further improve the performance of the method. The model is designated as the PMoEP enhanced low-rank model (PMoEP-LR). The Generalized Expectation–Maximization (GEM) algorithm is utilized to estimate the low-rank spectral correlation matrix and deduce all parameters of the PMoEP-LR model. Finally, the enhanced envelope spectrum (EES) is used to detect the defect characteristic. The efficacy of the proposed method is showcased by analyzing both simulated and real data.