Cyclostationary analysis is a useful approach in diagnostics of the machinery with rotating components. It allows indicating the cyclic modulations in the signal via analysis of a bi-frequency map called Cyclic Spectral Coherence (CSC). A non-zero CSC value at two frequencies means the presence of the cyclic process. Unfortunately, we have found that for some cyclostationary signals CSC provides difficult to interpret information. These disturbances in the CSC map have been linked to the presence of non-Gaussian noise. To prove it an original procedure has been proposed. Using simulations covering the model of signal, $\alpha$-stable distribution, and Monte Carlo simulations it has been shown that indeed increasing presence of non-Gaussian noise makes worse the quality of diagnostic information extracted from CSC map. It has been recalled that the Cyclic Spectral Coherence is based on the autocovariance function of a given signal, thus it is properly defined for data coming from the distribution with the finite second moment.

Finally, the authors selected three real examples that confirm the simulation-based findings. The main conclusion is before using the CSC analysis for cyclostationary signal one should validate the type of the noise. If noise is Gaussian — the CSC will bring optimal results. For the increasing level of impulsive non-cyclic noise, the CSC map becomes more and more disturbed and the detection of periodic excitation is difficult. Performed simulations on a very generic model some guidelines have been formulated regarding the acceptable level of non-Gaussian noise.

循环平稳分析是诊断具有旋转组件的机械的有用方法。它允许通过分析称为循环频谱相干性（CSC）的双频图来指示信号中的循环调制。两个频率上的CSC值非零表示存在循环过程。不幸的是，我们发现对于某些循环平稳信号，CSC提供了难以解释的信息。CSC图中的这些干扰与非高斯噪声的存在有关。为了证明这一点，已经提出了原始程序。使用涵盖信号模型的模拟，$\alpha$稳定的分布，以及蒙特卡洛模拟表明，确实增加的非高斯噪声的存在使从CSC图提取的诊断信息的质量变差。回想一下，循环频谱相干性基于给定信号的自协方差函数，因此，它是为来自具有有限第二矩的分布的数据正确定义的。

最后，作者选择了三个真实的例子来确认基于仿真的发现。主要结论是在将CSC分析用于循环平稳信号之前，应先验证噪声的类型。如果噪声是高斯-CSC将带来最佳结果。随着脉冲式非周期性噪声水平的提高，CSC映射变得越来越受干扰，并且很难检测到周期性激励。在非常通用的模型上执行的仿真，已经制定了一些有关可接受的非高斯噪声水平的准则。