Wheelset bearing plays a fairly important role in maintaining the safe and stable operation of high-speed trains. Thus, its fault detection is of great significance. Conventional boundary determination methods cannot assess fault information capacity contained in a decomposed band and thus provide a higher number of false boundaries than actually needed. A time-delayed kurtosis-based (TDK) adaptive boundary determination method (TDKABDM) is presented. The proposed TDK, is a measurement index for assessing fault information capacity within a noisy bandwidth. The boundaries determined by TDKABDM are then used to construct Meyer wavelets to extract fault-related resonance bands. The proposed method is validated by fault simulation signals and actual wheelset-bearing vibration signals. The results show that the proposed method can adaptively determine the reasonable boundaries of resonance bands containing fault information. Comparative analyses indicate that the proposed TDKABDM exhibits more excellent performance on extracting wheelset bearing fault characteristics.

轮对轴承在维持高速列车的安全和稳定运行中起着相当重要的作用。因此，其故障检测具有重要意义。传统的边界确定方法不能评估包含在已分解频带中的故障信息容量，因此提供的假边界数量要多于实际需要的数量。提出了一种基于时延峰态（TDK）的自适应边界确定方法（TDKABDM）。拟议的TDK是一种评估指标，用于评估噪声带宽内的故障信息容量。然后，将TDKABDM确定的边界用于构造Meyer小波，以提取与故障相关的共振带。通过故障仿真信号和实际的轮对轴承振动信号验证了该方法的有效性。结果表明，该方法可以自适应地确定包含故障信息的共振带的合理边界。比较分析表明，提出的TDKABDM在提取轮对轴承故障特征方面表现出更优异的性能。