Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization
Fault Detection and Behavior Analysis of Wheelset Bearing Using Adaptive Convolutional Sparse Coding Technique Combined with Bandwidth Optimization
复制标题
采用自适应卷积稀疏编码技术结合带宽优化的轮对轴承故障检测和行为分析
DOI：
10.1155/2020/8879732
复制
发表时间：
2020-11-18
影响因子：
1.6
通讯作者：
Tan, Andy C. C.
中科院分区：
文献类型：
--
作者：
He, Liu;Yi, Cai;Tan, Andy C. C.
轮对轴承是高速列车的关键部件，也是极易损坏的部件。轮对轴承故障诊断对保证高速列车安全运行、实现智能化运维具有重要意义。基于字典学习算法的卷积稀疏编码技术(CSCT-DLA)为轴承缺陷脉冲信号的提取提供了一种有效的算法框架。然而，字典学习容易受到地基振动和谐波干扰的影响，不能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参数的选择。卷积字典联合学习算法(UC-DLA)是CSCT-DLA中的一种有效算法。本文将UC-DLA用于轮对轴承故障检测，并对其进行了改进。最后，提出了一种新的轴承故障检测方法--自适应UC-DLA结合带宽优化方法(AUC-DLA-BO)。AUC-DLA-BO的数学模型是一种约束优化问题，可以克服地基振动和简谐干扰，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，消除与故障共振带相同频段的噪声，突出轴承故障脉冲。仿真信号和台架试验验证了该方法的有效性。结果表明，AUC-DLA-BO能够有效地检测轴承故障，实现对故障行为的精细分析。
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.