Spectral Correlation (SC) has become a critical analytical instrument for assessing the second-order cyclostationarity (CS) in signals, especially for mechanical vibration signals. Nonetheless, the computational demand for computing SC is considerable. Built upon the Smoothed Cyclic Periodogram (SCP), a consistent estimator named Fast Smoothed Cyclic Periodogram (FastSCP) is proposed to address this issue. FastSCP represents the computational process of SCP through the convolution of spectral components and incorporates the Overlap-Save algorithm for accelerating. Comparison results with other fast SC estimators, including Fast Spectral Correlation (FastSC) and Faster Spectral Correlation (FasterSC), indicate that the proposed method is competitive in terms of both computational cost and memory usage, particularly when dealing with wide cyclic frequency ranges.

中文翻译：

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通过频率平均快速计算频谱相关性


谱相关 (SC) 已成为评估信号（尤其是机械振动信号）二阶循环平稳性 (CS) 的关键分析工具。尽管如此，计算SC的计算需求是相当大的。基于平滑循环周期图（SCP），提出了一种名为快速平滑循环周期图（FastSCP）的一致估计器来解决这个问题。 FastSCP通过光谱分量的卷积来表示SCP的计算过程，并结合了Overlap-Save算法进行加速。与其他快速 SC 估计器（包括快速频谱相关（FastSC）和更快频谱相关（FasterSC））的比较结果表明，所提出的方法在计算成本和内存使用方面都具有竞争力，特别是在处理宽循环频率范围时。