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KLPP特征约简与RELM的高压隔膜泵单向阀故障诊断

李瑞 范玉刚 张光辉

李瑞,范玉刚,张光辉. KLPP特征约简与RELM的高压隔膜泵单向阀故障诊断[J]. 机械科学与技术,2023,42(8):1332-1339 doi: 10.13433/j.cnki.1003-8728.20220076
引用本文: 李瑞,范玉刚,张光辉. KLPP特征约简与RELM的高压隔膜泵单向阀故障诊断[J]. 机械科学与技术,2023,42(8):1332-1339 doi: 10.13433/j.cnki.1003-8728.20220076
LI Rui, FAN Yugang, ZHANG Guanghui. Check Valve Fault Diagnosis of High-pressure Diaphragm Pump with KLPP Feature Reduction and RELM[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(8): 1332-1339. doi: 10.13433/j.cnki.1003-8728.20220076
Citation: LI Rui, FAN Yugang, ZHANG Guanghui. Check Valve Fault Diagnosis of High-pressure Diaphragm Pump with KLPP Feature Reduction and RELM[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(8): 1332-1339. doi: 10.13433/j.cnki.1003-8728.20220076

KLPP特征约简与RELM的高压隔膜泵单向阀故障诊断

doi: 10.13433/j.cnki.1003-8728.20220076
基金项目: 国家自然科学基金项目(649220180003)
详细信息
    作者简介:

    李瑞(1996−),硕士研究生,研究方向为高压隔膜泵单向阀故障诊断,892310632@qq.com

    通讯作者:

    范玉刚,副教授,硕士生导师,ygfan@qq.com

  • 中图分类号: TN710.1;TH165.3

Check Valve Fault Diagnosis of High-pressure Diaphragm Pump with KLPP Feature Reduction and RELM

  • 摘要: 为此提出基于核局部保持投影(KLPP)和正则化极限学习机(RELM)的高压隔膜泵单向阀故障诊断方法。首先,提取单向阀振动信号的时域、频域、时频域特征,构建多域特征集;然后,通过KLPP算法对构建的多域特征集进行维数约简;最后,建立基于RELM的故障诊断模型,用于识别单向阀运行状态。实验结果表明,基于多域特征的故障诊断方法检测精度高于单域特征识别方法;KLPP约简多域特征集,可以有效消除信息冗余;建立的RELM故障诊断模型识别精度达到98.89%,能够有效识别高压隔膜泵单向阀故障类型。
  • 图  1  单向阀故障识别流程

    Figure  1.  The fault identification process for the one-way valve

    图  2  高压隔膜泵工作原理图

    Figure  2.  Working principle diagram of the high-pressure diaphragm pump

    图  3  3种状态时域振动信号

    Figure  3.  Time-domain vibration signals in three states

    图  4  3种算法降维可视化结果

    Figure  4.  The dimensionality reduction visualization results of three algorithms

    图  5  RELM模型故障识别结果

    Figure  5.  Fault identification results for the RELM model

    表  1  时域特征

    Table  1.   Time-domain features

    特征参数表达式特征参数表达式特征参数表达式
    平均值 ${T_1} = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {x(n)}$ 方差 ${T_7} = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N { { {\bigg( {x(n)} \bigg)}^2} }$ 脉冲指标 ${T_{13} } = \dfrac{ { {T_8} } }{ { {T_4} } }$
    标准差 ${T_2} = \sqrt {\dfrac{1}{ {N{{ - } }1} }\displaystyle\sum\limits_{n = 1}^N {\bigg[ {x(n) - {T_1} } \bigg]} }$ 最大值 $ {T_8} = \max \left| {x(n)} \right| $ 裕度指标 ${T_{14} } = \dfrac{ { {T_8} } }{ { {T_3} } }$
    方根幅值 ${T_{14} } = \dfrac{ { {T_8} } }{ { {T_3} } }$ 最小值 $ {T_9} = \min \left| {x(n)} \right| $ 偏度指标 ${T_{15} } = \dfrac{ { {T_5} } }{ { { {\bigg(\sqrt { {T_7} } \bigg)}^3} } }$
    绝对平均值 ${T_4} = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N {\left| {x(n)} \right|}$ 峰峰值 $ {T_{10}} = {T_8} - {T_9} $ 峭度指标 ${T_{16} } = \dfrac{ { {T_6} } }{ { { {\bigg(\sqrt { {T_7} } \bigg)}^2} } }$
    偏度 ${T_5} = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N { { {\bigg( {x(n)} \bigg)}^3} }$ 波形指标 ${T_{11} } = \dfrac{ { {T_2} } }{ { {T_4} } }$
    峭度 ${T_6} = \dfrac{1}{N}\displaystyle\sum\limits_{n = 1}^N { { {\bigg( {x(n)} \bigg)}^4} }$ 峰值指标 ${T_{12} } = \dfrac{ { {T_8} } }{ { {T_2} } }$
    下载: 导出CSV

    表  2  频域特征

    Table  2.   frequency-domain features

    特征参数表达式特征参数表达式特征参数表达式
    均值频率 ${F_1} = \dfrac{1}{K}\displaystyle\sum\limits_{k = 1}^K {s(k)}$ 频率特征6 ${F_6} = \sqrt {\dfrac{1}{K}\displaystyle\sum\limits_{k = 1}^K { { {\bigg({f_k} - {F_5}\bigg)}^2}s(k)} }$ 频率特征11 ${F_{11} } = \dfrac{ {\displaystyle\sum\limits_{k = 1}^K { { {\bigg({f_k} - {F_5}\bigg)}^3}s(k)} } }{ {K{ {({F_6})}^3} } }$
    频率中心 ${F_2} = \sqrt {\dfrac{1}{ {K - 1} }\displaystyle\sum\limits_{k = 1}^K { { {\bigg(s(k) - {F_1}\bigg)}^2} } }$ 频率特征7 ${F_7} = \sqrt {\dfrac{ {\displaystyle\sum\limits_{k = 1}^K {f_k^2s(k)} } }{ {\displaystyle\sum\limits_{k = 1}^K {s(k)} } } }$ 频率特征12 ${F_{12} } = \dfrac{ {\displaystyle\sum\limits_{k = 1}^K { { {\bigg({f_k} - {F_5}\bigg)}^4}s(k)} } }{ {K{ {({F_6})}^4} } }$
    标准差频率 ${F_3} = \dfrac{ {\displaystyle\sum\limits_{k = 1}^K { { {\bigg(s(k) - {F_1}\bigg)}^3} } } }{ {K{ {\bigg(\sqrt { {F_2} } \bigg)}^3} } }$ 频率特征8 ${F_8} = \sqrt {\dfrac{ {\displaystyle\sum\limits_{k = 1}^K {f_k^4s(k)} } }{ {\displaystyle\sum\limits_{k = 1}^K {f_k^2s(k)} } } }$ 频率特征13 ${F_{13} } = \dfrac{ {\displaystyle\sum\limits_{k = 1}^K {\sqrt {({f_k} - {F_5})s(k)} } } }{ {K{F_6} } }$
    频率特征4 ${F_4} = \dfrac{ {\displaystyle\sum\limits_{k = 1}^K { { {\bigg(s(k) - {F_1}\bigg)}^4} } } }{ {K{ {\bigg(\sqrt { {F_2} } \bigg)}^2} } }$ 频率特征9 ${F_9} = \dfrac{ {\displaystyle\sum\limits_{k = 1}^K {f_k^2s(k)} } }{ {\sqrt {\displaystyle\sum\limits_{k = 1}^K {s(k)} \displaystyle\sum\limits_{k = 1}^K {f_k^4s(k)} } } }$
    频率特征5 ${F_5} = \dfrac{ {\displaystyle\sum\limits_{k = 1}^K { {f_k}s(k)} } }{ {\displaystyle\sum\limits_{k = 1}^K {s(k)} } }$ 频率特征10 ${F_{10} } = \dfrac{ { {F_6} } }{ { {F_5} } }$
    下载: 导出CSV

    表  3  单向阀3种状态多域特征样本

    Table  3.   Multi-domain features of the one-way valve in three states

    状态样本特征值
    正常 1 [0.1770,0.2189,0.3480,0.2633,0.0631,0.0765,2.8497,1.8592,−0.9904,0.1211,1.3216,5.3422,7.0607,8.4909,1.4981,5.2204,0.0086,0.0004,9.7908,146.7420,290.9450,28.3471,420.9275,843.8580,0.4988,0.0974,13.5441,412.3895,0.02404,0.8562,0.0930,0.0138,0.0172,0.0004,0.0025,0.0096,0.0070,0.1328,0.2209,0.0591,0.0699,0.0035,0.0152,0.0446,0.0347]
    2 [0.1565,0.2278,0.3612,0.2728,0.0601,0.0893,3.2028,1.8960,−1.3068,0.1304,1.3239,5.2497,6.9500,8.3230,1.2767,5.2490,0.0097,0.0004,7.4746,94.6553,286.6772,27.6031,401.3355,798.7763,0.5024,0.0963,13.0351,408.7480,0.0261,0.7656,0.1220,0.0183,0.0767,0.0004,0.0014,0.0048,0.0108,0.2045,0.2567,0.0731,0.1970,0.0030,0.0089,0.0257,0.0489]
    击穿 1 [0.0951,0.0870,0.1239,0.1016,0.0028,0.0006,0.4525,0.3423,−0.1102,0.0153,1.2197,2.7634,3.3704,3.9352,1.4512,2.3941,0.0017,0.0001,17.7957,375.2469,173.9553,10.6549,308.9678,850.6089,0.3632,0.0613,52.1945,4199.5463,0.0064,0.9667,0.0293,0.0010,0.0017,0.0001,0.0002,0.0008,0.0003,0.0328,0.1033,0.0069,0.0110,0.0009,0.0015,0.0058,0.0021]
    2 [0.1143,0.1056,0.1387,0.1189,0.0036,0.0007,0.4575,0.3242,−0.1334,0.0192,1.1667,2.3369,2.7264,3.0707,1.3475,2.0194,0.0018,0.0001,19.6871,438.8704,183.7557,11.2595,321.8966,828.6363,0.3885,0.0613,45.3749,3345.3737,0.0068,0.9806,0.0141,0.0016,0.0020,0.0001,0.0002,0.0009,0.0005,0.0192,0.0600,0.0106,0.0122,0.0010,0.0017,0.0062,0.0039]
    卡阀 1 [0.0845,0.0807,0.111,0.0927,0.0019,0.0004,0.4356,0.2849,−0.1507,0.0123,1.1973,2.5666,3.0731,3.5289,1.4025,2.3104,0.0020,0.0001,17.7270,379.3032,218.0078,11.2845,335,750.0681,0.4466,0.0518,35.1537,2433.5674,0.0073,0.8993,0.0779,0.0072,0.0081,0,0.0002,0.0060,0.0013,0.0955,0.1988,0.0357,0.0391,0.0004,0.0014,0.0307, 0.0086]
    2 [0.0923,0.0915,0.1204,0.1030,0.0023,0.0005,0.4654,0.3179,−0.1475,0.0145,1.1686,2.6408,3.0861,3.4725,1.3412,2.1462,0.0020,0.0001,17.7732,379.7815,199.1707,11.2900,323.3263,781.6425,0.4136,0.05670,39.9310,2848.9510,0.0072,0.9427,0.0429,0.0039,0.0059,0,0.0001,0.0032,0.0013,0.0556,0.1351,0.0215,0.0301,0.0004,0.0012,0.0183,0.0085]
    下载: 导出CSV

    表  4  单域和多域特征RELM故障诊断结果

    Table  4.   RELM fault diagnosis results using single-domain and multi-domain features

    特征提取方法故障识别精度/%
    正常击穿卡阀平均
    时域 83.33 83.33 66.67 77.78
    频域 80 86.67 73.33 80
    时频域 80 70 63.33 71.11
    多域 90 90 76.67 85.56
    下载: 导出CSV

    表  5  多域特征降维后故障诊断结果

    Table  5.   Fault diagnosis results after dimensionality reduction of multi-domain features

    方法故障识别精度/%
    正常击穿卡阀平均
    KPCA-ELM 93.33 90 83.33 88.89
    KPCA-RELM 93.33 93.33 83.33 90
    LPP-ELM 96.67 93.33 90 93.33
    LPP-RELM 96.67 96.67 90 94.44
    KLPP-ELM 100 96.67 93.33 96.67
    KLPP-RELM 100 100 96.67 98.89
    下载: 导出CSV
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  • 收稿日期:  2021-08-11
  • 刊出日期:  2023-08-31

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