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基于改进复合多尺度样本熵的行星齿轮箱故障诊断

李伟 王付广 王东生

李伟, 王付广, 王东生. 基于改进复合多尺度样本熵的行星齿轮箱故障诊断[J]. 航空动力学报, 2024, 39(9):20220691 doi: 10.13224/j.cnki.jasp.20220691
引用本文: 李伟, 王付广, 王东生. 基于改进复合多尺度样本熵的行星齿轮箱故障诊断[J]. 航空动力学报, 2024, 39(9):20220691 doi: 10.13224/j.cnki.jasp.20220691
LI Wei, WANG Fuguang, WANG Dongsheng. Fault diagnosis of planetary gearbox based on improved composite multi-scale sample entropy[J]. Journal of Aerospace Power, 2024, 39(9):20220691 doi: 10.13224/j.cnki.jasp.20220691
Citation: LI Wei, WANG Fuguang, WANG Dongsheng. Fault diagnosis of planetary gearbox based on improved composite multi-scale sample entropy[J]. Journal of Aerospace Power, 2024, 39(9):20220691 doi: 10.13224/j.cnki.jasp.20220691

基于改进复合多尺度样本熵的行星齿轮箱故障诊断

doi: 10.13224/j.cnki.jasp.20220691
基金项目: 国家自然科学基金(51205198); 安徽省自然科学基金(2008085ME149); 安徽省高校科研基金(2023AH051665); 平台建设协同创新项目(GXXT-2022-090); 校级自然科研项目(2022tlxy49)
详细信息
    作者简介:

    李伟(1993-),男,讲师,硕士,主要从事故障诊断和撞击动力学等方面的研究

  • 中图分类号: V232.8;TH165+.3;TN911.7

Fault diagnosis of planetary gearbox based on improved composite multi-scale sample entropy

  • 摘要:

    针对多尺度样本熵受样本长度影响较大,且粗粒化过程较粗糙,易忽略有效信息的不足,在复合多尺度样本熵的基础上,以采样点间能量分布作为权重进行粗粒化计算,提出了改进的复合多尺度样本熵,并将其应用于行星齿轮箱故障诊断。通过仿真信号研究不同参数和不同噪声特性对改进复合多尺度样本熵算法的影响,将其与多尺度样本熵、广义多尺度样本熵、复合多尺度样本熵进行对比,验证了本文改进算法的稳定性。结合变分模态分解、主成分分析和支持向量机对行星齿轮箱实验信号进行故障诊断。对比结果表明:所提方法能够有效地实现不同工况和不同结构行星齿轮箱太阳轮常见故障诊断,且故障识别率达到95%以上,具有一定的有效性。

     

  • 图 1  改进复合多尺度样本熵特征提取流程图

    Figure 1.  Flow chart of improved composite multi-scale sample entropy feature extraction

    图 2  改进粗粒化示意图

    Figure 2.  Schematic diagram of improved coarse granulation

    图 3  不同噪声背景下,样本长度N对MSE、GMSE、CMSE和ICMSE分析结果影响

    Figure 3.  Effect of sample length N on MSE, GMSE, CMSE and ICMSE analysis results under different noise backgrounds

    图 4  粉噪声背景下,不同相似容限r对MSE、GMSE、CMSE和ICMSE分析结果影响

    Figure 4.  Influence of different similarity tolerance r on MSE, GMSE, CMSE and ICMSE analysis results under thepowder noise background

    图 5  白噪声背景下,不同相似容限r对MSE、GMSE、CMSE和ICMSE分析结果影响

    Figure 5.  Influence of different similarity tolerance r on MSE, GMSE, CMSE and ICMSE analysis results under white noise background

    图 6  基于ICMSE的故障诊断流程图

    Figure 6.  ICMSE based fault diagnosis flow chart

    图 7  实验台示意图

    Figure 7.  Schematic diagram of test bench

    图 8  太阳轮故障示意图

    Figure 8.  Fault diagram of sun gear

    图 9  缺齿故障惩罚因子、分解维数与皮尔逊相关系数变化趋势

    Figure 9.  Change trend of penalty factor, decomposition dimension and Pearson correlation coefficient in normal state

    图 10  20 Hz转频下太阳轮常见故障不同熵值分析

    Figure 10.  Analysis of different entropy values of common faults of sun gear at 20 Hz rotating frequency

    图 11  30Hz转频下单级行星齿轮箱太阳轮常见故障ICMSE分析结果

    Figure 11.  ICMSE analysis results of faults of sun gear of single-stage planetary gearbox at 30 Hz rotating frequency

    图 12  20 Hz转频下双级行星齿轮箱太阳轮常见故障ICMSE分析结果

    Figure 12.  ICMSE analysis results of faults of sun gear of two-stage planetary gearbox at 20 Hz rotating frequency

    图 13  30 Hz转频下双级行星齿轮箱太阳轮常见故障ICMSE分析结果

    Figure 13.  ICMSE analysis results of faults of sun gear of two-stage planetary gearbox at 30 Hz rotating frequency

    图 14  MSE特征集降重后第一、二主成分分量

    Figure 14.  First and second principal components of MSE feature set after dimension reduction

    图 15  GMSE特征集降重后第一、二主成分分量

    Figure 15.  First and second principal components of GMSE feature set after dimension reduction

    图 16  CMSE特征集降重后第一、二主成分分量

    Figure 16.  First and second principal components of CMSE feature set after dimension reduction

    图 17  ICMSE特征集降重后第一、二主成分分量

    Figure 17.  First and second principal components of ICMSE feature set after dimension reduction

    表  1  20 Hz转频下太阳轮常见故障对应VMD参数

    Table  1.   VMD parameters corresponding to common faults of sun gear at 20 Hz rotating frequency

    算法参数正常裂纹缺齿磨损
    K4566
    α2250275017501750
    下载: 导出CSV

    表  2  fim与原始信号的相关系数和欧氏距离

    Table  2.   Correlation coefficient and Euclidean distance between fim and original signal

    分量 相关系数 欧氏距离
    fim1 0.8153 0.4892
    fim2 0.7703 0.6077
    fim3 0.7033 0.6924
    fim4 0.6567 0.7512
    fim5 0.5614 0.8092
    fim6 0.4756 0.8331
    下载: 导出CSV

    表  3  支持向量机多故障分类器对4种太阳轮故障识别结果(Qtrain1Qtest1

    Table  3.   Recognition results of support vector machine multi fault classifier for four kinds of sun gear faults (Qtrain1Qtest1

    特征提取
    方法
    cg SVM多故障分类器对测试样本识别结果/%
    正常状态识别率 裂纹故障识别率 缺齿故障识别率 磨损故障识别率 平均故障识别率
    MSE (48.50,0.57) 80 55 95 90 80
    GMSE (16,1.5) 90 90 100 55 83.75
    CMSE (27.8,7.5) 80 90 100 90 90
    ICMSE (62.7,3.2) 80 100 100 100 95
    下载: 导出CSV

    表  4  基于ICMSE和SVM的识别结果

    Table  4.   Recognition results based on ICMSE and SVM

    特征提取
    方法
    随机样本 cg SVM多故障分类器对测试样本识别结果/%
    正常状态识别率 裂纹故障识别率 缺齿故障识别率 磨损故障识别率 平均故障识别率
    ICMSE Qtrain2Qtest2 (67,4.7) 87 100 100 97 96
    Qtrain3Qtest3 (48,6.2) 83 100 100 100 95.75
    下载: 导出CSV
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  • 收稿日期:  2022-09-15
  • 网络出版日期:  2023-11-08

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