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.

中文翻译：

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旋转机械故障诊断中的振动冲击扰动建模：广义混合高斯模型


在现实工业环境中，复杂的背景噪声由各种成分组成，偏离简单的高斯分布（如冲击噪声）。在这项工作中，开发了一种基于指数幂混合 (MoEP) 分布的鲁棒噪声建模方法来解决这个问题。为了熟练地提取故障特征，通过Fast-SC获得信号的二维表示，将故障特征的低秩性和复杂噪声结合在信号模型中。然后，结合噪声模型的惩罚函数以进一步提高该方法的性能。该模型被指定为 PMoEP 增强低秩模型（PMoEP-LR）。利用广义期望最大化（GEM）算法来估计低秩谱相关矩阵并推导 PMoEP-LR 模型的所有参数。最后，利用增强包络谱（EES）来检测缺陷特征。通过分析模拟和真实数据展示了所提出方法的有效性。