As one of the crucial subsystems, the performance of the wheelset-bearing system will significantly affect the running safety, stability and comfort of a high-speed train. Compared with the traditional rotating machinery vibration signals, those of wheelset-bearing systems have a distinct difference, for instance, the wheelset tread damage induced vibrations and bearing fault-related vibrations coexist and have similar wave-forms. Therefore, it is necessary to advance a reasonable method to identify the two kinds of cyclo-stationary components. To achieve this purpose, the adaptive correlated Kurtogram (ACK) is proposed. The proposed ACK method has two advantages: the first one is it can adaptively generate the paving of the plane for Kurtogram by using scale-space representation (SSR) to guarantee the detected boundaries cover the resonant frequency band well; the second one is it can highlight the special periodic impulses by correlated kurtosis. The scale-scale plane (SSP), which is obtained by SSR, is used to represent the frequency distribution characteristic of the acquired signal, firstly. After that, the paving of the plane is arbitrarily divided based on the SSP. Then the empirical wavelet transform (EWT) is employed to construct the sub-band signals according to the paving of the plane. The correlated kurtosis values with interesting periodic are calculated to distinguish the special repetitive transients. The nodes with the largest correlated kurtosis values are used to conduct a further envelope spectrum analysis. ACK method improves the effectiveness of the identification of the frequency bands containing impact information, and is capable of detecting multiple frequency bands simultaneously. The proposed ACK method is demonstrated by the simulated signal and two experimental signals acquired from a wheelset-bearing system test bench. The results indicate that the ACK can simultaneously extract the wheelset tread damage and bearing fault-related vibration component, and it is superior to the other traditional methods.

作为关键子系统之一，轮对轴承系统的性能将极大地影响高速列车的运行安全性，稳定性和舒适性。与传统的旋转机械振动信号相比，轮对轴承系统的振动信号具有明显的差异，例如，轮对胎面损伤引起的振动与轴承故障相关的振动共存并具有相似的波形。因此，有必要提出一种合理的方法来识别两种环-固定组件。为了实现该目的，提出了自适应相关Kurtogram（ACK）。所提出的ACK方法具有两个优点：第一，它可以通过使用比例空间表示（SSR）来自适应地生成适用于Kurtogram的平面铺装，以确保检测到的边界很好地覆盖了共振频带；第二个是它可以通过相关峰度突出特殊的周期性冲动。通过SSR获得的标度标度平面（SSP）首先用于表示所获取信号的频率分布特性。此后，根据SSP任意分割平面的铺装。然后，根据平面的铺展，采用经验小波变换（EWT）构造子带信号。计算具有有趣周期的相关峰度值以区分特殊的重复瞬态。具有最大相关峰度值的节点用于进行进一步的包络频谱分析。ACK方法提高了包含影响信息的频带的识别效率，并且能够同时检测多个频带。通过模拟信号和从轮对轴承系统测试台获得的两个实验信号证明了所提出的ACK方法。结果表明，ACK可以同时提取轮对胎面损伤和轴承故障相关的振动分量，优于其他传统方法。具有最大相关峰度值的节点用于进行进一步的包络频谱分析。ACK方法提高了包含影响信息的频带的识别效率，并且能够同时检测多个频带。通过模拟信号和从轮对轴承系统测试台获得的两个实验信号证明了所提出的ACK方法。结果表明，ACK可以同时提取轮对胎面损伤和轴承故障相关的振动分量，优于其他传统方法。具有最大相关峰度值的节点用于进行进一步的包络频谱分析。ACK方法提高了包含影响信息的频带的识别效率，并且能够同时检测多个频带。通过模拟信号和从轮对轴承系统测试台获得的两个实验信号证明了所提出的ACK方法。结果表明，ACK可以同时提取轮对胎面损伤和轴承故障相关的振动分量，优于其他传统方法。并且能够同时检测多个频段。通过模拟信号和从轮对轴承系统测试台获得的两个实验信号证明了所提出的ACK方法。结果表明，ACK可以同时提取轮对胎面损伤和轴承故障相关的振动分量，优于其他传统方法。并且能够同时检测多个频段。通过模拟信号和从轮对轴承系统测试台获得的两个实验信号证明了所提出的ACK方法。结果表明，ACK可以同时提取轮对胎面损伤和轴承故障相关的振动分量，优于其他传统方法。