Parameter-adaptive VME and Its Application in Fault Diagnosis of Axle Box Bearings

doi:10.3969/j.issn.1004-132X.2026.09.005

Abstract

Abstract:

To address the issue that the effectiveness of fault feature extraction of VME method in axle box bearing fault diagnosis was affected by manually preset parameters， a parameter-adaptive variational mode extraction method was proposed based on the improved mean kurtosis. An improved mean kurtosis index that does not require predefining the number of subsegment divisions was utilized to adaptively determine the optimal parameter combination of penalty factor and center frequency， and the desired mode with the highest improved mean kurtosis value was subjected to envelope spectrum analysis to diagnose its fault type. The proposed method was applied to the processing of experimental data pertaining to three different fault types in axle box bearings for locomotives， metros， and passenger trains. The results show that the proposed method may effectively solve the parameter selection problem of VME and accurately extract the fault characteristics of three different types of axle box bearings. Compared with the Autogram and CFFsgram methods， the superiority of the proposed method in extracting fault frequency and its harmonic frequencies of axle box bearings is verified.

Key words: axle box bearing, improved mean kurtosis, variational mode extraction（VME）, parameter optimization, fault diagnosis

摘要：

针对变分模态提取方法在轴箱轴承故障诊断时故障特征提取效果易受人为预设参数影响的问题，提出了一种基于改进平均峭度指标的参数自适应变分模态提取方法。采用一种无需预设子片段分割数量的改进平均峭度指标自适应确定惩罚因子和中心频率的最佳参数组合，并将与最大改进平均峭度值相对应的期望模态进行包络谱分析以诊断其故障类型。将所提方法分别应用于机车、地铁和客车轴箱轴承的3种不同故障类型试验数据分析过程，结果表明：所提方法能够有效解决变分模态提取方法的参数选取问题，并准确提取3种不同类型轴箱轴承的故障特征。通过与Autogram和CFFsgram方法对比，验证了所提方法在提取轴箱轴承故障频率及其倍频信息方面的优越性。

关键词: 轴箱轴承, 改进平均峭度, 变分模态提取, 参数优化, 故障诊断

CLC Number:

TH17

HE Yong, LI Yang, WANG Hong. Parameter-adaptive VME and Its Application in Fault Diagnosis of Axle Box Bearings[J]. China Mechanical Engineering, 2026, 37(9): 2124-2132.

何勇, 李洋, 王红. 参数自适应变分模态提取及其在轴箱轴承故障诊断中的应用[J]. 中国机械工程, 2026, 37(9): 2124-2132.

Figures/Tables 14

Fig.1 Calculation process of improved mean kurtosis

Fig.2 VME parameter optimization process

Fig.3 Components of the mixed signal f1

Fig.4 Time domain waveform of mixed signal f1

Fig.5 Envelope spectrum of mixed signal f1

Fig.6 Test bench of locomotive axle box bearing

Fig.7 Envelope spectrum of locomotive axle box bearing

Fig.8 Envelope spectrum of metro train axle box bearing

Fig.9 Test bench of passenger train axle box bearing

Fig.10 Envelope spectrum of passenger train axle box bearing

Fig.11 Envelope spectrum of the mode based on median kurtosis optimization

Fig.12 Envelope spectrum of the mode based on kurtosis optimization of square envelope spectrum

Fig. 13 Diagnosis results of different types of axle box bearings using lower Autogram method

Fig.14 Diagnosis results of different types of axle box bearings using CFFsgram method

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