全文下载： 
202604014.pdf
文章编号：1672-6987（2026）04-0106-09；DOI：10. 16351/j. 1672-6987. 2026. 04. 014
汤武初，韩 丹，吕亚博，刘佳彬 （大连交通大学 机械工程学院，辽宁 大连 116028）
摘 要：为解决在噪声干扰下，无法准确提取故障特征信息的问题，提出了一种集成经验模态 分解（ensemble empirical mode decomposition，EEMD）和白鲸算法（belug whale optimization， BWO）优化变分模态分解（variational modal decomposition，VMD）相结合的铁路货车轴承故障 诊断方法。首先对原始故障信号进行 EEMD 分解得到若干分量，根据筛选指标重构信号，去掉 部分噪声干扰。然后选择最小包络熵为目标函数，利用 BWO 优化 VMD 参数组。最后使用优 化后的 VMD 对重构信号进行分解得到若干个本征模态函数（intrinsic mode function，IMF），选 择合适的本征模态函数进行包络解调，进而可通过其包络谱准确提取出故障特征信息。以铁 路货车轴承 353130B 为试验对象，采用大连交通大学铁路轴承动态性能试验机设备，对轴承的 内圈和滚动体两种故障类型进行试验分析，结果表明：在现有试验条件下，该方法在噪声干扰 下能够准确地将铁路货车轴承的故障特征信息提取出来，从而实现铁路货车轴承的故障诊断。
关键词：铁路货车轴承；故障诊断；集成经验模态分解；白鲸算法；优化变分模态分解
中图分类号：TH 165+. 3； TH 133. 3 文献标志码：A
引用格式：汤武初，韩丹，吕亚博，等 . 基于 EEMD 和 BWO‑VMD 的铁路货车轴承故障诊断 ［J］. 青岛科技大学学报（自然科学版），2026，47（4）：106-114.
TANG Wuchu， HAN Dan， LYU Yabo， et al. Fault diagnosis of railway freight car bearings based on EEMD and BWO-VMD［J］. Journal of Qingdao University of Science and Technol‑ ogy（Natural Science Edition），2026，47（4）：106-114.
Fault Diagnosis of Railway Freight Car Bearings Based on EEMD and BWO-VMD
TANG Wuchu，HAN Dan，LYU Yabo，LIU Jiabin （College of Mechanical Engineering， Dalian Jiaotong University， Dalian 116028， China）
Abstract：In order to solve the problem that the fault characteristic information cannot be accu‑ rately extracted under the interference of noise. An ensemble empirical mode decomposition （EEMD） and belug whale optimization （BWO） algorithm are proposed. BWO optimization of the combination of variational modal decomposition （VMD） railway truck bearing fault diagno‑ sis method. Firstly， the original fault signal is decomposed by EEMD to obtain several compo‑ nents， and then the signal is reconstructed according to the screening index to remove some noise interference. Then the minimum envelope entropy is selected as the objective function，and the VMD parameter set is optimized by BWO. Finally， the optimized VMD is used to decompose the reconstructed signal to obtain several intrinsic mode functions （IMF）， and the appropriate intrinsic mode functions are selected for envelope demodulation， and then the fault characteristic information can be accurately extracted through its envelope spectrum. In this paper， the railway truck bearing 353130B is used as the test object， and the railway bearing dynamic performance testing machine equipment of Dalian Jiaotong University is used to test and analyze the two fault types of the bearing inner ring and the rolling body. The results show that： Under the present test conditions， the method can accurately extract the fault characteris‑ tic information of railway freight car bearings under the interference of noise， and diagnose the fault type of railway freight car bearings.
Key words：railway truck bearing； fault diagnosis； ensemble empirical mode decomposition （EEMD）； belug whale optimization （BWO）；variational modal decomposition （VMD）
收稿日期：2025-11-02
基金项目：辽宁省科技厅计划项目（101300268）.
作者简介：汤武初（1973—），男，副教授 .
