留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于AESL-GA的BN球磨机滚动轴承故障诊断方法

王进花 汤国栋 曹洁 李亚洁

王进花,汤国栋,曹洁,等. 基于AESL-GA的BN球磨机滚动轴承故障诊断方法[J]. 北京航空航天大学学报,2024,50(4):1138-1146 doi: 10.13700/j.bh.1001-5965.2022.0428
引用本文: 王进花,汤国栋,曹洁,等. 基于AESL-GA的BN球磨机滚动轴承故障诊断方法[J]. 北京航空航天大学学报,2024,50(4):1138-1146 doi: 10.13700/j.bh.1001-5965.2022.0428
WANG J H,TANG G D,CAO J,et al. Fault diagnosis method of BN ball mill rolling bearing based on AESL-GA[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1138-1146 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0428
Citation: WANG J H,TANG G D,CAO J,et al. Fault diagnosis method of BN ball mill rolling bearing based on AESL-GA[J]. Journal of Beijing University of Aeronautics and Astronautics,2024,50(4):1138-1146 (in Chinese) doi: 10.13700/j.bh.1001-5965.2022.0428

基于AESL-GA的BN球磨机滚动轴承故障诊断方法

doi: 10.13700/j.bh.1001-5965.2022.0428
基金项目: 国家重点研发计划(2020YFB1713600);国家自然科学基金(62063020);甘肃省自然科学基金(20JR5RA463)
详细信息
    通讯作者:

    E-mail:wjh0615@lut.edu.cn

  • 中图分类号: TP277;TD453

Fault diagnosis method of BN ball mill rolling bearing based on AESL-GA

Funds: National Key R & D Program of China (2020YFB1713600); National Natural Science Foundation of China (62063020);Gansu Natural Science Foundation (20JR5RA463)
More Information
  • 摘要:

    针对基于知识的贝叶斯网络(BN)构建方法存在不完全和不精确的缺点,提出一种基于知识引导和数据挖掘的BN结构构建方法。针对单一信号故障诊断结果不精确的问题和故障信息中存在的不确定性问题,将电流信号与振动信号融合建立BN的特征节点,分别提取2种信号的故障特征参数,利用区分度指标法进行特征筛选,将其作为BN结构特征层的节点。将专家知识构建的初始BN结构结合自适应精英结构遗传算法(AESL-GA)进行结构优化,通过自适应限制进化过程中的搜索空间,减少自由参数的数量,提高其全局搜索能力,得到最优BN结构。通过MQY5585溢流型球磨机滚动轴承实测数据和Paderborn University轴承数据集对所提方法进行验证,结果证明了所提方法的有效性。

     

  • 图 1  BN模型简略图

    Figure 1.  Brief diagram of BN model

    图 2  基于专家知识的球磨机滚动轴承初始BN结构

    Figure 2.  Initial BN structure of ball mill rolling bearing based on expert knowledge

    图 3  BN故障诊断模型构建流程

    Figure 3.  BN fault diagnosis model construction process

    图 4  基于本文方法的球磨机BN故障诊断模型

    Figure 4.  BN fault diagnosis model of ball mill based on the proposed method

    图 5  基于球磨机数据的混淆矩阵

    Figure 5.  Confusion matrix under ball mill data

    图 6  球磨机数据下不同模型的诊断精度及运行时间对比

    Figure 6.  Comparison of diagnostic accuracy and running time of different models under ball mill data

    图 7  基于专家知识的Paderborn University轴承数据集初始BN结构

    Figure 7.  Paderborn University bearing dataset Initial BN structure of based on expert knowledge

    图 8  基于本文方法在Paderborn University数据集下的BN故障诊断模型

    Figure 8.  BN fault diagnostic model under Paderborn University dataset based on the proposed method

    图 9  基于Paderborn University数据集下的混淆矩阵

    Figure 9.  Confusion matrix based on Paderborn University dataset

    图 10  不同工况下各类故障诊断识别率对比

    Figure 10.  Comparison of various fault diagnosis accuracy rates under different working conditions

    图 11  Paderborn University数据集下不同模型的诊断精度及运行时间

    Figure 11.  Diagnostic accuracy and running time of different models under Paderborn University dataset

    表  1  振动信号特征参数D

    Table  1.   Characteristic parameter D value of vibration signal

    运行工况 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10
    C1 4.82 3.21 1.77 1.69 1.46 3.44 4.95 1.38 2.15 1.58
    C2 3.53 2.37 1.21 1.22 1.80 3.27 3.62 1.44 2.04 2.25
    C3 2.51 2.89 2.07 2.73 0.65 2.11 2.20 2.04 1.33 1.31
    下载: 导出CSV

    表  2  电流信号特征参数D

    Table  2.   Characteristic parameter D value of current signal

    运行工况 S1 S2 S3 S4 S5 S6 S7 S8 S9 S10
    C1 3.65 2.16 1.87 1.22 1.18 3.31 2.06 1.42 1.14 1.78
    C2 2.85 1.69 1.59 2.06 2.25 2.56 1.71 2.16 2.17 1.96
    C3 2.14 0.31 2.76 1.14 0.97 2.94 1.95 1.97 1.56 0.65
    下载: 导出CSV

    表  3  推理测试结果

    Table  3.   Reasoning test results

    测试
    序号
    测试故障 与故障相关的
    异常征兆
    故障类型 后验概率%
    1 内圈 P3(高),P4(高),P5(低) 内圈故障 94.25
    2 外圈 P1(高),P4(低),P6(高) 外圈故障 92.18
    3 正常 P1(高),P2(高) 正常状态 97.42
    下载: 导出CSV

    表  4  基于本文方法的球磨机数据故障识别率

    Table  4.   Fault recognition rate of ball mill data based on the proposed method

    工况故障类型识别率%
    C1正常状态98.43
    内圈故障97.72
    外圈故障98.55
    C2正常状态98.56
    内圈故障97.83
    外圈故障98.43
    C3正常状态98.66
    内圈故障98.17
    外圈故障98.50
    下载: 导出CSV

    表  5  数据集故障描述

    Table  5.   Dataset fault description

    故障节点标签 故障类型
    F0 正常(Nor)
    F1 外圈钻孔(OD)
    F2 内圈电雕刻(IE),外圈电雕刻(OE)
    F3 内圈放电沟槽 (IEDM),外圈放电沟槽(OEDM)
    F4 外圈塑化(OP)
    F5 内圈点蚀(IFP),外圈点蚀(OFP)
    下载: 导出CSV

    表  6  特征量筛选结果

    Table  6.   Feature selection results

    故障节点标签 筛选出的特征
    F0 S1S3S4S5S8S9
    F1 S1S2S3
    F2 S3S4S9
    F3 S1S4S6
    F4 S1S4S7S8
    F5 S5S7S8S9
    下载: 导出CSV

    表  7  Paderborn University数据集下4种工况条件时的故障识别率

    Table  7.   Fault recognition rate under 4 working conditions in Paderborn University dataset %

    故障类型 C1 C2 C3 C4
    正常 97.86 97.73 97.77 97.83
    外圈钻孔 95.88 96.18 96.43 97.44
    内圈电雕刻 96.85 96.28 96.79 96.94
    外圈电雕刻 96.89 96.46 96.83 96.78
    内圈放电沟槽 96.54 95.93 96.87 96.44
    外圈放电沟槽 96.58 96.63 96.40 96.72
    外圈塑化 87.38 87.13 90.92 91.83
    内圈点蚀 89.68 88.39 88.57 90.44
    外圈点蚀 89.78 89.58 89.65 91.83
    下载: 导出CSV
  • [1] DAI X W, GAO Z W. From model, signal to knowledge: A data-driven perspective of fault detection and diagnosis[J]. IEEE Transactions on Industrial Informatics, 2013, 9(4): 2226-2238. doi: 10.1109/TII.2013.2243743
    [2] BARTLETT L M, HURDLE E E, KELLY E M. Integrated system fault diagnostics utilising digraph and fault tree-based approaches[J]. Reliability Engineering & System Safety, 2009, 94(6): 1107-1115.
    [3] BEN ALI J, FNAIECH N, SAIDI L, et al. Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals[J]. Applied Acoustics, 2015, 89(9): 16-27.
    [4] ZHANG W D, ZHANG F, CHEN W, et al. Fault state recognition of rolling bearing based fully convolutional network[J]. Computing in Science & Engineering, 2019, 21(5): 55-63.
    [5] XU G W, LIU M, JIANG Z F, et al. Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning[J]. Sensors, 2019, 19(5): 1088. doi: 10.3390/s19051088
    [6] PEARL J. Bayesian networks: A model cf self-activated memory for evidential reasoning[C]//Proceedings of the 7th Conference of the Cognitive Science Society. Irvine: University of California, 1985: 15-17.
    [7] CAI B P, LIU H L, XIE M. A real-time fault diagnosis methodology of complex systems using object-oriented Bayesian networks[J]. Mechanical Systems and Signal Processing, 2016, 80(1): 31-44.
    [8] 鞠萍华, 柯磊, 冉琰, 等. 基于动作单元的机电产品故障溯源诊断方法[J]. 湖南大学学报(自然科学版), 2020, 47(2): 60-66.

    JU P H, KE L, RAN Y, et al. Method of fault root causes tracing analysis for electro-mechanical products based on action unit[J]. Journal of Hunan University (Natural Sciences), 2020, 47(2): 60-66(in Chinese).
    [9] SAHU A R, PALEI S K. Real-time fault diagnosis of HEMM using Bayesian network: A case study on drag system of dragline[J]. Engineering Failure Analysis, 2020, 118(12): 104917.
    [10] WANG J X, WANG Z W, STETSYUK V, et al. Exploiting Bayesian networks for fault isolation: A diagnostic case study of diesel fuel injection system[J]. ISA Transactions, 2019, 86(5): 276-286.
    [11] XIAO Y, PAN W G, GUO X M, et al. Fault diagnosis of traction transformer based on Bayesian network[J]. Energies, 2020, 13(18): 4966. doi: 10.3390/en13184966
    [12] CAI B P, LIU Y H, FAN Q, et al. Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network[J]. Applied Energy, 2014, 114(2): 1-9.
    [13] CHEN B, XIE L, LI Y Z, et al. Acoustical damage detection of wind turbine yaw system using Bayesian network[J]. Renewable Energy, 2020, 160(7): 1364-1372.
    [14] 朱彦沛, 邢宁哲, 纪雨彤, 等. 基于交互式主动探测的电力综合数据网故障定位算法[J]. 电力系统自动化, 2017, 41(4): 35-40.

    ZHU Y P, XING N Z, JI Y T, et al. Fault location algorithm of integrated data network for power system based on interactive active detection[J]. Automation of Electric Power Systems, 2017, 41(4): 35-40(in Chinese).
    [15] 王金鑫, 王忠巍, 马修真, 等. 柴油机燃油系统多故障的解耦与诊断技术[J]. 控制与决策, 2019, 34(10): 2249-2255.

    WANG J X, WANG Z W, MA X Z, et al. Decoupling and diagnosis of multi-fault of diesel engine fuel system[J]. Control and Decision, 2019, 34(10): 2249-2255(in Chinese).
    [16] CAI B P, HUANG L, XIE M. Bayesian networks in fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2017, 13(5): 2227-2240. doi: 10.1109/TII.2017.2695583
    [17] 刘浩然, 马明, 李世昭, 等. 一种应用于故障诊断中的高效推理算法[J]. 控制与决策, 2015, 30(11): 2033-2040.

    LIU H R, MA M, LI S Z, et al. An effective inference algorithm for fault diagnosis[J]. Control and Decision, 2015, 30(11): 2033-2040(in Chinese).
    [18] 王双成, 唐海燕, 刘喜华. 用于风险管理的贝叶斯网络学习[J]. 控制与决策, 2007, 22(5): 569-572. doi: 10.3321/j.issn:1001-0920.2007.05.019

    WANG S C, TANG H Y, LIU X H. Learning Bayesian networks in risk management[J]. Control and Decision, 2007, 22(5): 569-572(in Chinese). doi: 10.3321/j.issn:1001-0920.2007.05.019
    [19] 王海羽, 刘浩然, 张力悦, 等. 基于节点块序列约束的局部贝叶斯网络结构搜索算法[J]. 自动化学报, 2020, 46(6): 1210-1219.

    WANG H Y, LIU H R, ZHANG L Y, et al. Local Bayesian network structure searching using constraint of node chunk sequence[J]. Acta Automatica Sinica, 2020, 46(6): 1210-1219(in Chinese).
    [20] 刘浩然, 孙美婷, 李雷, 等. 基于蚁群节点寻优的贝叶斯网络结构算法研究[J]. 仪器仪表学报, 2017, 38(1): 143-150. doi: 10.3969/j.issn.0254-3087.2017.01.019

    LIU H R, SUN M T, LI L, et al. Study on Bayesian network structure learning algorithm based on ant colony node order optimization[J]. Chinese Journal of Scientific Instrument, 2017, 38(1): 143-150(in Chinese). doi: 10.3969/j.issn.0254-3087.2017.01.019
    [21] HUANG W C, KOU X Y, ZHANG Y, et al. Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach[J]. Reliability Engineering & System Safety, 2021, 205(2): 107250.
    [22] SPIRTES P, GLYMOUR C, SCHEINES R. Causation, prediction, and search[M]. Cambridge: The MIT Press, 2001.
    [23] LIU M K, TRAN M Q, WENG P Y. Fusion of vibration and current signatures for the fault diagnosis of induction machines[J]. Shock and Vibration, 2019, 2019: 7176482.
    [24] 赵志宏. 基于振动信号的机械故障特征提取与诊断研究[D]. 北京: 北京交通大学, 2012.

    ZHAO Z H. Research on vibration signal based machinery fault feature extraction and diagnosis[D]. Beijing: Beijing Jiaotong University, 2012 (in Chinese).
    [25] LI K, CHEN P, WANG S M. An intelligent diagnosis method for rotating machinery using least squares mapping and a fuzzy neural network[J]. Sensors, 2012, 12(5): 5919-5939. doi: 10.3390/s120505919
    [26] CONTALDI C, VAFAEE F, NELSON P C. Bayesian network hybrid learning using an elite-guided genetic algorithm[J]. Artificial Intelligence Review, 2019, 52(1): 245-272. doi: 10.1007/s10462-018-9615-5
    [27] LI T T, ZHAO Y, ZHANG C B, et al. A knowledge-guided and data-driven method for building HVAC systems fault diagnosis[J]. Building and Environment, 2021, 198(5): 107850.
    [28] LESSMEIER C, KIMOTHO J K, ZIMMER D, et al. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification[C]//Proceedings of the European Conference of the Prognostics and Health Management Society. New York: PHM Society, 2016: 1-17.
    [29] HOANG D T, KANG H J. A motor current signal-based bearing fault diagnosis using deep learning and information fusion[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(6): 3325-3333. doi: 10.1109/TIM.2019.2933119
  • 加载中
图(11) / 表(7)
计量
  • 文章访问数:  163
  • HTML全文浏览量:  92
  • PDF下载量:  12
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-05-28
  • 录用日期:  2022-07-10
  • 网络出版日期:  2022-09-05
  • 整期出版日期:  2024-04-29

目录

    /

    返回文章
    返回
    常见问答