Vibration signals from rotating machineries are usually of multi-component and modulated signals. Hilbert–Huang transform (HHT), hereby referring to the combination of empirical mode decomposition (EMD) and normalized Hilbert transform (NHT), is an effective method to extract useful information from the multi-component and modulated signals. However, sifting stopping criterion (SSC) that is crucial to the HHT performance has not been well explored for this sift-driven method in the past decades. This paper proposes the soft SSC, which can ease the mode-mixing problem in signal decomposition through the EMD and improve demodulation performance in signal demodulation. The soft SSC can adapt to input signals and determine the optimal iteration number of a sifting process by tracking this sifting process. Extensive simulations show that the soft SSC can enhance the performance of the HHT in signal decomposition, signal demodulation, and the estimation of the instantaneous amplitude and frequency over the existing state-of-the-art SSCs. Finally, the improved HHT with the soft SSC is demonstrated on the fault diagnosis of wheelset bearings.

来自旋转机械的振动信号通常是多分量调制信号。希尔伯特-黄变换（HHT），特指经验模态分解（EMD）和归一化希尔伯特变换（NHT）的结合，是一种从多分量调制信号中提取有用信息的有效方法。然而，在过去的几十年中，对于这种筛选驱动的方法，对 HHT 性能至关重要的筛选停止标准 (SSC) 并未得到很好的探索。本文提出了软SSC，通过EMD可以缓解信号分解中的模式混合问题，提高信号解调中的解调性能。软 SSC 可以适应输入信号并通过跟踪筛选过程来确定筛选过程的最佳迭代次数。大量仿真表明，与现有的最先进的 SSC 相比，软 SSC 可以提高 HHT 在信号分解、信号解调以及瞬时幅度和频率估计方面的性能。最后，在轮对轴承故障诊断中展示了改进的软SSC HHT。