The second-order synchrosqueezing S-transform (SSST2) is an important method for instantaneous frequency (IF) estimation of non-stationary signals. Based on the synchrosqueezing S-transform, the instantaneous frequency calculation method is modified using the second-order partial derivatives of time and frequency to achieve higher frequency resolution. However, weak multi-frequency signals with strong background noise are often drowned out during the transformation process. To achieve enhanced extraction of weak fault characteristic signals due to mechanical faults, this paper proposes an optimally weighted sliding window signal segmentation algorithm based on the SSST2. The results of simulations and experiments show that the time–frequency aggregation of the second-order synchrosqueezing S-transform based on the optimally weighted sliding window (OWSW-SSST2) is not only significantly higher than that of commonly used time–frequency transforms, but it also has better operational efficiency than the second-order synchrosqueezing S-transform. In this paper, the proposed algorithm is used to analyze fault signals from actual high-speed railway wheelset bearings. The results show that the OWSW-SSST2 algorithm greatly improves the spectral aggregation of the signal, and crucially, that high-precision IF estimates for signals can be obtained in low signal-to-noise ratio environments. This research is both of academic interest and significant for practical engineering use to ensure safe high-speed rail operations. It helps enable monitoring the status of wheelset bearings, correctly estimating the locations and causes of failures, and providing up-to-date systematic maintenance and system improvement strategies.

二阶同步压缩 S 变换 (SSST2) 是非平稳信号瞬时频率 (IF) 估计的重要方法。在同步挤压S变换的基础上，利用时间和频率的二阶偏导数修改瞬时频率计算方法，以实现更高的频率分辨率。然而，在变换过程中往往会淹没带有强背景噪声的弱多频信号。为实现对机械故障引起的弱故障特征信号的增强提取，提出一种基于SSST2的最优加权滑动窗口信号分割算法。仿真和实验结果表明，基于最优加权滑动窗口（OWSW-SSST2）的二阶同步挤压S变换的时频聚合不仅显着高于常用的时频变换，而且它还具有比二阶同步挤压 S 变换更好的运行效率。在本文中，所提出的算法用于分析来自实际高速铁路轮对轴承的故障信号。结果表明，OWSW-SSST2算法极大地改善了信号的频谱聚合，至关重要的是，可以在低信噪比环境下获得信号的高精度中频估计。这项研究既具有学术意义，又对实际工程应用具有重要意义，以确保安全的高速铁路运营。