Spectral amplitude modulation (SAM) method, as an automated and empirical nonlinear filtering approach, has shown great promise for rotating machinery fault diagnosis. However, due to the inherent shortcomings of Fourier transform in SAM, leading to significant errors in the edited amplitude, also it is easy to fail with selecting the optimal weight value manually under intense background noise. To solve the aforementioned drawbacks, the Stockwell transform spectral amplitude modulation (STSAM) method is proposed. The Stockwell transform (S-transform) is first utilized to obtain the phase and amplitude with time–frequency information. Then, the edited signals can be reconstructed by inverse S-transform with the above actual phase and the modified amplitudes under different weights. Hence, more comprehensive and accurate information about the amplitude can be computed. After that, their normalized square envelope spectra under each cyclic frequency are calculated to showcase the fault characteristics. Moreover, a novel indicator is proposed to automatically choose the optimal weight in STSAM, thus clearer characteristic frequencies can be represented by the optimal square envelope spectrum (OSES). Finally, the effectiveness and superiority of the STSAM and OSES methods are systematically demonstrated by the comparative studies with the SAM and time–frequency SAM approaches using simulated signals and real-world datasets.

中文翻译：

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旋转机械故障诊断的斯托克韦尔变换谱调幅方法


频谱幅度调制（SAM）方法作为一种自动化的、经验性的非线性滤波方法，在旋转机械故障诊断方面显示出了巨大的前景。然而，由于SAM中傅里叶变换的固有缺陷，导致编辑的幅度误差较大，并且在强背景噪声下手动选择最佳权值也很容易失败。为了解决上述缺点，提出了斯托克韦尔变换频谱幅度调制（STSAM）方法。首先利用斯托克韦尔变换（S 变换）来获取具有时频信息的相位和幅度。然后，利用上述实际相位和不同权重下的修改幅度，通过反S变换可以重构编辑后的信号。因此，可以计算出关于幅度的更全面和准确的信息。之后，计算每个循环频率下的归一化方包络谱以展示故障特征。此外，提出了一种新的指标来自动选择STSAM中的最佳权重，从而可以通过最佳方包络谱（OSES）来表示更清晰的特征频率。最后，通过使用模拟信号和真实数据集与 SAM 和时频 SAM 方法进行比较研究，系统地证明了 STSAM 和 OSES 方法的有效性和优越性。