Wheelset bearing is a critical and easily damaged component of a high-speed train. Wheelset bearing fault diagnosis is of great significance to ensure safe operation of high-speed trains and realize intelligent operation and maintenance. The convolutional sparse coding technique based on the dictionary learning algorithm (CSCT-DLA) provides an effective algorithm framework for extracting the impulses caused by bearing defect. However, dictionary learning is easily affected by foundation vibration and harmonic interference and cannot learn the key structure related to fault impulses. At the same time, the detection performance of fault impulse heavily depends on the selection of parameters in this approach. Union of convolutional dictionary learning algorithm (UC-DLA) is an efficient algorithm in CSCT-DLA. In this paper, UC-DLA is introduced and improved for wheelset bearing fault detection. Finally, a novel bearing fault detection method, adaptive UC-DLA combined with bandwidth optimization (AUC-DLA-BO), is proposed. The mathematical formulation of AUC-DLA-BO is a sort of constrained optimization problem, which can overcome foundation vibration and harmonic interference and adaptively determine parameters related to UC-DLA. The proposed method can detect the fault resonance band adaptively, eliminate the noise with the same frequency band as the fault resonance band, and highlight the bearing fault impulses. Simulated signals and bench tests are used to verify the effectiveness of the proposed method. The results show that AUC-DLA-BO can effectively detect bearing faults and realize the refined analysis of fault behavior.

中文翻译：

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自适应卷积稀疏编码结合带宽优化的轮对轴承故障检测与行为分析

轮对轴承是高速火车的重要组成部分，容易损坏。轮对轴承故障诊断对确保高速列车的安全运行，实现智能化的运维具有重要意义。基于字典学习算法（CSCT-DLA）的卷积稀疏编码技术为提取轴承缺陷引起的脉冲提供了有效的算法框架。但是，词典学习容易受到基础振动和谐波干扰的影响，无法学习与故障脉冲相关的关键结构。同时，故障脉冲的检测性能在很大程度上取决于该方法中的参数选择。卷积字典联合学习算法（UC-DLA）是CSCT-DLA中的一种有效算法。在本文中，引入并改进了UC-DLA，用于轮对轴承故障检测。最后，提出了一种新的轴承故障检测方法，即自适应UC-DLA结合带宽优化（AUC-DLA-BO）。AUC-DLA-BO的数学公式是一种约束优化问题，可以克服地基振动和谐波干扰，自适应地确定与UC-DLA相关的参数。该方法能够自适应地检测出故障共振带，消除了与故障共振带相同频带的噪声，并突出了轴承故障脉冲。仿真信号和基准测试用于验证所提方法的有效性。结果表明，AUC-DLA-BO可以有效地检测轴承故障，实现对故障行为的精细分析。