Wheelset bearing is one of the crucial rotating elements in the train bogie. Detection of wheelset bearing defect comes with many challenges due to complex wheel/rail excitation and the horrible working condition. The parametric dictionary sparse representation provides a practical path to detect the weak fault of wheelset bearing. However, the parametric dictionary obtained by the classical correlation filtering algorithm (CFA) is hard to match the analyzed signal’s underlying fault impact characteristic. A novel parametric dictionary design algorithm named fault impact matching algorithm (FIMA) combining Orthogonal matching pursuit (OMP) is proposed to address the problem in this paper. The core of the FIMA mainly comprises two stages: partial segmentation and global analysis. Two indexes, correlation function (CF) and kurtosis, are used to comprehensively evaluate the partial and global matching degree between the Laplace wavelet and the signal. The proposed method’s effectiveness is verified by the fault simulation analysis and the practical wheelset bearing fault signals (outer and inner race fault). Some comparison studies demonstrate that the proposed method outperforms the CFA–OMP, the K-SVD–OMP and some time–frequency decomposition methods, such as EWT and VMD, in detecting the bearing weak defects.

轮对轴承是火车转向架中至关重要的旋转元件之一。由于复杂的轮/轨激励和恶劣的工作条件，轮对轴承缺陷的检测面临许多挑战。参数字典的稀疏表示为检测轮对轴承的弱故障提供了实用的途径。但是，通过经典相关滤波算法（CFA）获得的参数字典很难匹配分析信号的潜在故障影响特性。针对此问题，提出了一种新颖的参数字典设计算法，称为故障影响匹配算法（FIMA），结合了正交匹配追踪（OMP）。FIMA的核心主要包括两个阶段：部分细分和整体分析。相关函数（CF）和峰度两个指标，用于全面评估拉普拉斯小波与信号之间的局部和全局匹配程度。通过故障仿真分析和实际的轮对轴承故障信号（外部和内圈故障）验证了该方法的有效性。一些比较研究表明，该方法在检测轴承的弱缺陷方面优于CFA–OMP，K-SVD–OMP和某些时频分解方法，例如EWT和VMD。