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航空发动机磨损故障多目标融合诊断

马佳丽 陈果 康玉祥 王雨薇 苗慧慧 曹桂松

马佳丽, 陈果, 康玉祥, 等. 航空发动机磨损故障多目标融合诊断[J]. 航空动力学报, 2024, 39(10):20220191 doi: 10.13224/j.cnki.jasp.20220191
引用本文: 马佳丽, 陈果, 康玉祥, 等. 航空发动机磨损故障多目标融合诊断[J]. 航空动力学报, 2024, 39(10):20220191 doi: 10.13224/j.cnki.jasp.20220191
MA Jiali, CHEN Guo, KANG Yuxiang, et al. Multi-objective fusion diagnosis of aeroengine wear failure[J]. Journal of Aerospace Power, 2024, 39(10):20220191 doi: 10.13224/j.cnki.jasp.20220191
Citation: MA Jiali, CHEN Guo, KANG Yuxiang, et al. Multi-objective fusion diagnosis of aeroengine wear failure[J]. Journal of Aerospace Power, 2024, 39(10):20220191 doi: 10.13224/j.cnki.jasp.20220191

航空发动机磨损故障多目标融合诊断

doi: 10.13224/j.cnki.jasp.20220191
基金项目: 国家科技重大专项(J2019-Ⅳ-004-0071); 国家自然科学基金(51675263); 中国航发商用航空发动机有限责任公司项目
详细信息
    作者简介:

    马佳丽(1998-),女,硕士生,主要从事航空发动机故障诊断方面的研究

  • 中图分类号: V263.6

Multi-objective fusion diagnosis of aeroengine wear failure

  • 摘要:

    针对多种油液分析数据的特点,建立了航空发动机磨损故障融合诊断方法,实现基于油液分析数据的航空发动机磨损状态综合评估。该故障融合诊断方法包括磨损故障定性分析、定位分析和定因分析。定性分析以光谱、铁谱和颗粒计数原始分析数据为输入,基于(Dempster-Shafer)证据理论获得发动机磨损故障定性诊断结果;在定位分析部分,建立了基于深度学习的滚动轴承故障部位识别模型,以能谱分析原始数据作为模型输入,实现了航空发动机磨损部位的智能识别;最后,在定性分析部分,利用定性结果和定位结果,根据领域专家的经验,建立了基于if-then的知识规则,找出发动机磨损故障原因;利用实际油液监测数据对所提方法的有效性和可靠性进行验证,诊断精度最高可达到100%,结果充分表明了该方法的正确性、有效性。

     

  • 图 1  基于多种油液分析数据的航空发动机磨损故障多目标融合诊断流程图

    Figure 1.  Multi-objective fusion diagnosis of aeroengine wear fault based on multiple oil analysis data oil analysis flow chart

    图 2  自定义隶属函数

    Figure 2.  User defined membership function

    图 3  一维卷积神经网络训练流程图

    Figure 3.  One-dimensional convolutional neural network training flow chart

    图 4  模型测试结果

    Figure 4.  Model test results

    图 5  试验轴承

    Figure 5.  Test bearing

    图 6  数据采集装置

    Figure 6.  Data acquisition device

    表  1  各种油液分析数据对故障诊断的有效性分析

    Table  1.   Effectiveness analysis of various oil analysis data for fault diagnosis

    参数 光谱数据 颗粒计数数据 铁谱数据 能谱数据 理化数据
    定性 *** *** *** *
    定位 * ***
    定因 ** *** ***
    注:“***”表示有效性最高;“**”表示有效性中等;“*”表示有效性较差;“—”为无效。
    下载: 导出CSV

    表  2  规则可信度

    Table  2.   Rule credibility

    证据 规则可信度 故障分析方法
    Fe 0.5 光谱分析
    Cr 0.05
    Pb 0.05
    Cu 0.1
    Sn 0.05
    Al 0.05
    Ni 0.02
    Ti 0.02
    Mn 0.05
    Ag 0.02
    Si 0.05
    Mg 0.02
    Mo 0.02
    疲劳磨粒 0.4 铁谱分析
    球状磨粒 0.1
    层状磨粒 0.1
    红色氧化物 0.2
    黑色氧化物 0.2
    >5 μm 0.1 颗粒计数分析
    >15 μm 0.2
    >25 μm 0.3
    >50 μm 0.4
    下载: 导出CSV

    表  3  一维卷积残差网络参数

    Table  3.   One dimensional convolution residual network Parameters

    结构 卷积核参数 输出大小
    第1层 (1×3×64)×2 1×31
    第2层 (1×3×128)×2 1×15×128
    第3层 (1×3×256)×2 1×7×256
    第4层 (1×3×512)×2 1×3×512
    FC 512×3×1 1536×1
    LSTM 1536×3×200 200×1
    FC 全连接层 29×1
    下载: 导出CSV

    表  4  油液性能参数变化与故障原因对照表

    Table  4.   Comparison between changes of oil performance parameters and fault causes

    油液分析类型 油液参数变化 故障发生原因
    铁谱分析 疲劳、球状和层状磨粒数量高于警告限 滚动轴承疲劳剥落失效
    红色氧化物数量高于警告限 润滑油中混有水分
    黑色氧化物数量高于警告限 润滑油供应不足,长时间高温高负载工作
    理化分析 水分含量高于警告限 润滑油中混有水分
    杂质含量高于警告限 润滑油中混有硬质颗粒杂质
    黏度含量高于警告限 润滑油供应不足,长时间高温高负载工作
    黏度含量低于正常限 润滑油中混有水分
    酸值含量高于警告限 润滑油供应不足
    下载: 导出CSV

    表  5  元素故障界限值

    Table  5.   Element fault limit value

    属性 铁谱分析(个/mL) 理化分析
    疲劳
    磨粒
    球状
    磨粒
    层状
    磨粒
    红色
    氧化物
    黑色
    氧化物
    黏度/
    (mm2/s)
    酸度/
    (mg/kg)
    闪点
    t/℃
    水分/
    (mg/kg)
    杂质/
    %
    正常值 2 3 1 1 1 25 0.05 258 0 0
    警告值 4 5 2 3 2 22.5/27.5 0.15 273 0.001 0.1
    下载: 导出CSV

    表  6  用于定性诊断的油液数据(部分数据)

    Table  6.   Fluid data for qualitative diagnosis (partial data)

    数据 光谱分析/‰ 铁谱分析(个/mL) 颗粒计数分析(个/100 mL)
    Fe
    Ag
    Cu
    Cr
    疲劳
    磨粒
    球状
    磨粒
    层状
    磨粒
    红色
    氧化物
    >5 μm >15 μm >25 μm >50 μm
    1 1.054 0.056 0.62 0.36 2.6 3.45 0.64 2.05 2501.255 181.213 92.736 8.931
    2 6.039 0.033 4.33 0.12 3 3.3 0.62 2.23 1799.37 522.769 290.427 11.405
    3 1.162 0.079 0.932 0.21 2.9 3.4 0.53 2.13 2011.551 42.114 89.304 10.713
    4 0.582 0.059 1.313 0 3 3.36 0.64 1.69 5630.736 213.183 203.268 13.845
    5 0.674 0.081 2.063 0.27 2.7 3.38 0.60 1.52 3112.509 88.515 91.591 15.163
    6 1.008 0.094 2.316 0.3 3.6 3.93 0.66 2.1 3358.563 101.445 109.875 19.181
    7 0.799 0.056 2.605 0.29 3.1 3.85 1.22 2.1 3512.164 233.834 114.392 22.239
    40 6.674 0.203 9.99 0.29 7.4 5.2 1.95 2.4 8088.109 1221.251 396.159 51.003
    下载: 导出CSV

    表  7  用于定位诊断的油液数据(部分数据)

    Table  7.   Fluid data for location diagnosis (partial data)

    数据 Cu Zn Al Mn Fe Sn Cr Mo V O C
    1 0 0 0 0 89.99 0 4.10 4.80 1.11 0 0
    2 0 0 0 0 89.63 0 4.25 4.78 1.35 0 0
    3 0 0 0 0 93.81 0 3.54 2.11 0.54 0 0
    4 61.98 31.17 3.00 1.84 1.37 0.64 0 0 0 0 0
    5 0 0 0 0 69.21 0 0 0 0 30.79 0
    6 0 0 0 0 66.45 0 0 0 0 28.67 4.88
    7 0 0 63.97 0 17.63 0 0 0 0 4.73 13.67
    40 0 0 0 0 94.04 0 0 0.14 0.11 0 5.71
    下载: 导出CSV

    表  8  用于定因诊断的油液数据(部分数据)

    Table  8.   Fluid data for cause determination diagnosis (partial data)

    数据 铁谱分析(个/mL) 理化分析
    疲劳
    磨粒
    球状
    磨粒
    层状
    磨粒
    红色
    氧化物
    黑色
    氧化物
    黏度/
    (mm2/s)
    酸度/
    (mg/kg)
    闪点
    t/℃
    水分/
    (mg/kg)
    杂质/%
    1 2.6 3.45 0.64 2.05 0.17 24.87 0.05 256.37 0 0.03
    2 3 3.3 0.62 2.23 0 24.83 0.04 256.72 0 0.01
    3 2.9 3.4 0.53 2.13 0.28 25.13 0.05 257.3 0 0.1
    4 3 3.36 0.64 1.69 0.04 25.01 0.07 258.82 0 0.1
    5 2.7 3.38 0.60 1.52 0.03 25.14 0.05 257.78 0 0.12
    6 3.6 3.93 0.66 2.1 0.09 25.17 0.05 258.49 0 0.11
    7 3.1 3.85 1.22 2.1 0.26 25.12 0.05 259.92 0 0.12
    40 7.4 5.2 1.95 2.4 0.95 25.62 0.05 269.43 0 0.155
    下载: 导出CSV

    表  9  光谱诊断结果

    Table  9.   Spectral diagnosis results

    证据ABC光谱诊断结果
    Fe10.50.5
    0.661
    Ag0.05170.020.001
    Cu10.10.1
    Cr0.490.050.245
    下载: 导出CSV

    表  10  铁谱诊断结果

    Table  10.   Ferrographic diagnosis results

    证据ABC铁谱诊断结果
    疲劳磨粒10.40.5

    0.743
    球状磨粒0.780.10.312
    层状磨粒0.650.10.065
    红色氧化物0.720.20.144
    黑色氧化物0.330.20.066
    下载: 导出CSV

    表  11  颗粒计数诊断结果

    Table  11.   Particle count diagnostic results

    证据 A B C 颗粒计数诊断结果
    >5 μm 1 0.1 0.1
    0.696
    >15 μm 0.98 0.2 0.196
    >25 μm 1 0.3 0.3
    >50 μm 1 0.4 0.4
    下载: 导出CSV

    表  12  光谱和铁谱融合诊断结果

    Table  12.   Fusion diagnosis results of spectrum and ferrographic

    证据ABC光谱和铁谱融合诊断结果
    Fe10.50.5



    0.912
    Ag0.05170.020.001
    Cu10.10.1
    Cr0.490.050.245
    疲劳磨粒10.40.5
    球状磨粒0.780.10.312
    层状磨粒0.650.10.065
    红色氧化物0.720.20.144
    黑色氧化物0.330.20.066
    下载: 导出CSV

    表  13  定性融合诊断结果

    Table  13.   Qualitative fusion diagnosis results

    证据 A B C 定性融合诊断结果
    Fe 1 0.5 0.5






    0.973
    Ag 0.0517 0.02 0.001
    Cu 1 0.1 0.1
    Cr 0.49 0.05 0.245
    疲劳磨粒 1 0.4 0.5
    球状磨粒 0.78 0.1 0.312
    层状磨粒 0.65 0.1 0.065
    红色氧化物 0.72 0.2 0.144
    黑色氧化物 0.33 0.2 0.066
    >5 μm 1 0.1 0.1
    >15 μm 0.98 0.2 0.196
    >25 μm 1 0.3 0.3
    >50 μm 1 0.4 0.4
    下载: 导出CSV

    表  14  定位诊断结果

    Table  14.   Location diagnosis results

    序号 Resnet18 Resnet34 LSTM CNN 本文模型
    1 0Cr18Ni9 ZL114A-T6 1Cr13 1Cr17Ni2 1Cr11Ni2W2MoV
    84% 93% 90% 82% 95%
    2 镍石墨 1Cr11Ni2W2MoV 2Cr13 40CrNiMoA 2Cr13
    77% 89% 86% 79% 91%
    3 1Cr12Ni2WMoVNb 18Cr2Ni4WA ZG1Cr18Ni9Ti 1Cr13 1Cr13
    77% 76% 84% 66% 89%
    4 1Cr17Ni2 1Cr17Ni2 1Cr11Ni2W2MoV 1Cr18Ni9Ti 40CrNiMoA
    72% 74% 79% 64% 85%
    5 1Cr11Ni2W2MoV 1Cr12Ni2WMoVNb 1Cr17Ni2 2Cr13 1Cr17Ni2
    65% 71% 73% 53% 80%
    下载: 导出CSV

    表  15  定因诊断结果

    Table  15.   Causal diagnosis results

    元素值 是否处于
    异常状态
    疲劳磨粒数量>4(个/mL)
    球状磨粒数量>5(个/mL)
    层状磨粒数量<2(个/mL)
    红色氧化物数量<3(个/mL)
    黑色氧化物数量<2(个/mL)
    22.2 (mm2/s)<黏度<27.5(mm2/s)
    酸值<0.15 (mg/kg)
    闪点值<273(t/℃)
    水分含量<0.001(mg/kg)
    杂质含量<0.1(%)
    下载: 导出CSV
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    ZHANG Xining, XIANG Zhou, TANG Chunhua. A deep convolutional auto-encoding neural network and its application in bearing fault diagnosis[J]. Journal of Xi’an Jiaotong University, 2018, 52(7): 1-8, 59. (in Chinese) doi: 10.7652/xjtuxb201807001
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出版历程
  • 收稿日期:  2022-04-04
  • 网络出版日期:  2024-05-20

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