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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用于旋转机械故障诊断的 Stockwell 变换光谱幅度调制方法


频谱幅度调制 （SAM） 方法作为一种自动化的经验非线性滤波方法，在旋转机械故障诊断方面显示出巨大的前景。然而，由于 SAM 中傅里叶变换的固有缺点，导致编辑幅度出现重大误差，在强烈的背景噪声下手动选择最佳权重值也很容易失败。针对上述缺点，该文提出斯托克韦变换谱幅度调制（STSAM）方法。首先使用 Stockwell 变换（S 变换）来获取相位和振幅以及时频信息。然后，通过逆 S 变换，将上述实际相位和修改后的幅度在不同权重下进行重构。因此，可以计算出有关振幅的更全面和准确的信息。之后，计算它们在每个循环频率下的归一化方包络谱，以展示断层特征。此外，提出了一种新的指标来自动选择 STSAM 中的最优权重，从而可以用最optimal square envelope spectrum（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世界数据集的 SAM 和时频 SAM 方法的比较研究，系统地证明了 STSAM 和 OSES 方法的有效性和优越性。