Deng Feiyue, Qiang Yawen, Yang Shaopu, Hao Rujiang, Liu Yongqiang
State Key Laboratory of Mechanical behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; Hebei Province Key Laboratory of Mechanical Power and Transmission Control, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China.
ISA Trans. 2021 Apr;110:368-378. doi: 10.1016/j.isatra.2020.10.034. Epub 2020 Oct 21.
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.
轮对轴承是列车转向架中关键的旋转部件之一。由于轮轨激励复杂且工作条件恶劣，轮对轴承缺陷检测面临诸多挑战。参数化字典稀疏表示为检测轮对轴承的微弱故障提供了一条实用途径。然而，通过经典相关滤波算法（CFA）获得的参数化字典难以匹配被分析信号潜在的故障影响特征。本文提出一种结合正交匹配追踪（OMP）的名为故障影响匹配算法（FIMA）的新型参数化字典设计算法来解决该问题。FIMA的核心主要包括两个阶段：局部分割和全局分析。使用相关函数（CF）和峭度这两个指标来综合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轮对轴承故障信号（外圈和内圈故障）验证了所提方法的有效性。一些对比研究表明，在检测轴承微弱缺陷方面，所提方法优于CFA - OMP、K - SVD - OMP以及一些时频分解方法，如经验小波变换（EWT）和变分模态分解（VMD）。
