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小样本下基于孪生神经网络的柱塞泵故障诊断

高浩寒 潮群 徐孜 陶建峰 刘明阳 刘成良

高浩寒,潮群,徐孜,等. 小样本下基于孪生神经网络的柱塞泵故障诊断[J]. 北京航空航天大学学报,2023,49(1):155-164 doi: 10.13700/j.bh.1001-5965.2021.0213
引用本文: 高浩寒,潮群,徐孜,等. 小样本下基于孪生神经网络的柱塞泵故障诊断[J]. 北京航空航天大学学报,2023,49(1):155-164 doi: 10.13700/j.bh.1001-5965.2021.0213
GAO H H,CHAO Q,XU Z,et al. Piston pump fault diagnosis based on Siamese neural network with small samples[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):155-164 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0213
Citation: GAO H H,CHAO Q,XU Z,et al. Piston pump fault diagnosis based on Siamese neural network with small samples[J]. Journal of Beijing University of Aeronautics and Astronautics,2023,49(1):155-164 (in Chinese) doi: 10.13700/j.bh.1001-5965.2021.0213

小样本下基于孪生神经网络的柱塞泵故障诊断

doi: 10.13700/j.bh.1001-5965.2021.0213
基金项目: 国家重点研发计划(2020YFB2007202)
详细信息
    通讯作者:

    E-mail: jftao@sjtu.edu.cn

  • 中图分类号: TH322;TH17

Piston pump fault diagnosis based on Siamese neural network with small samples

Funds: National Key R & D Program of China (2020YFB2007202)
More Information
  • 摘要:

    针对目前基于深度神经网络的柱塞泵故障诊断方法在小样本条件下精度低、模型欠拟合问题,提出一种小样本条件下基于孪生神经网络的柱塞泵故障诊断方法。搭建了柱塞泵故障实验台,采集柱塞泵在不同健康状态下的壳体振动信号;使用由卷积层和池化层组成孪生子网络自适应地从原始振动信号中提取低维特征,使用欧式距离判定输入样本对的特征相似度;通过相似度对比的方法扩大训练样本数量并训练孪生神经网络模型;最后,对测试样本进行健康状态识别。实验结果表明:与传统深度神经相比,所提方法在小样本情况下具有更高的准确率。同时,多通道数据融合实验表明:所提方法能够从不同通道的信号中学习到有关故障信息,多通道数据融合可以进一步提高诊断准确率。

     

  • 图 1  柱塞泵实验台原理

    Figure 1.  Schematic diagram of axial piston pump test bench

    图 2  振动传感器的安装

    Figure 2.  Installation of acceleration sensor

    图 3  不同健康状态的振动信号

    Figure 3.  Vibration signals for different health states

    图 4  典型卷积神经网络结构

    Figure 4.  Typical structure of a convolutional neural network

    图 5  孪生神经网络结构

    Figure 5.  Structure of Siamese neural network

    图 6  孪生神经网络故障诊断模型结构

    Figure 6.  Structure of fault diagnosis model based on Siamese neural network

    图 7  孪生神经网络故障诊断流程

    Figure 7.  Process of fault diagnosis based on Siamese neural network

    图 8  训练集与测试集划分

    Figure 8.  Division of training set and testing set

    图 9  训练样本数量对诊断结果的影响

    Figure 9.  Influence of number of training samples on diagnosis results

    图 10  通道数量对诊断结果的影响

    Figure 10.  Influence of channel number on diagnosis results

    图 11  提取特征的T-SNE可视化

    Figure 11.  T-SNE visualization of extracted features

    图 12  模型诊断结果混淆矩阵

    Figure 12.  Confusion matrix of model diagnosis results

    表  1  被测泵的额定参数

    Table  1.   Rated parameters of tested pump

    参数数值
    柱塞数9
    排量/(mL·r−1)1.2
    额定转速/(r·min−1)1 000
    出口压力/MPa21
    进口压力/MPa0.25
    下载: 导出CSV

    表  2  被测泵的健康状态、故障注入方式与状态标签

    Table  2.   Health states, fault injection modes and labels of tested pump

    健康状态注入方式/ mm标签
    正常0
    滑靴副轻度磨损摩擦副间隙0.051
    滑靴副中度磨损摩擦副间隙0.152
    滑靴副严重磨损摩擦副间隙0.203
    配流副轻度磨损转子弹簧剪短1.04
    配流副中度磨损转子弹簧剪短3.65
    配流副严重磨损转子弹簧剪短5.66
    下载: 导出CSV

    表  3  孪生神经网络主要结构参数

    Table  3.   Main structural parameters of Siamese neural network

    网络层关键参数激活函数
    卷积层1卷积核为32,数目为16,步长为8ReLU
    池化层1池化核为2
    卷积层2卷积核为3,数目为16,步长为1ReLU
    池化层2池化核为2
    卷积层3卷积核为2,数目为32,步长为1ReLU
    池化层3池化核为2
    卷积层4卷积核为3,数目为32,步长为1ReLU
    池化层4池化核为2
    卷积层5卷积核为3,数目为64,步长为1ReLU
    池化层5池化核为2
    全连接层神经元数量为100sigmoid
    输出层神经元数量为1sigmoid
    下载: 导出CSV

    表  4  数据集划分

    Table  4.   Data set division

    健康状态状态标签训练集数量测试集数量
    正常060030
    滑靴副轻度磨损160030
    滑靴副中度磨损260030
    滑靴副严重磨损360030
    配流副轻度磨损460030
    配流副中度磨损560030
    配流副严重磨损660030
    下载: 导出CSV

    表  5  不同训练样本数量下对各模型的诊断结果

    Table  5.   Diagnosis results of models under different number of training samples

    诊断模型不同训练样本数量下的识别准确率/%
    35701402102803504907001 4002 1002 8003 5004 200
    SVM34.0043.1450.5960.0064.1067.1475.1483.2495.6298.9599.8099.90100
    1D-CNN36.9556.9563.4389.1488.8692.7697.1490.8697.1498.1099.9098.29100
    本文所提方法70.8678.3890.6791.7191.4393.5295.1494.6897.7198.5798.6799.90100
    下载: 导出CSV

    表  6  不同通道数量下的诊断结果

    Table  6.   Diagnosis results under different number of channels

    诊断模型不同训练样本数量下的识别准确率/%
    35701402102803504907001 4002 1002 8003 5004 200
    单通道70.8678.3890.6791.7191.4393.5295.1494.8697.7198.5798.6799.90100
    双通道70.3491.2496.4896.1098.6798.5797.9098.6799.9099.2498.8699.2499.43
    三通道72.5796.1097.4398.8699.6299.6210010010099.9010099.90100
    下载: 导出CSV
  • [1] 彭熙伟, 陈建萍. 液压技术的发展动向[J]. 液压与气动, 2007(3): 1-5. doi: 10.3969/j.issn.1000-4858.2007.03.001

    PENG X W, CHEN J P. The future trends of hydraulics[J]. Chinese Hydraulics & Pneumatics, 2007(3): 1-5(in Chinese). doi: 10.3969/j.issn.1000-4858.2007.03.001
    [2] TANG S N, ZHU Y, YUAN S Q, et al. Intelligent diagnosis towards hydraulic axial piston pump using a novel integrated CNN model[J]. Sensors, 2020, 20(24): 7152. doi: 10.3390/s20247152
    [3] KUMAR S, BERGADA J M, WATTON J. Axial piston pump grooved slipper analysis by CFD simulation of three-dimensional NVS equation in cylindrical coordinates[J]. Computers & Fluids, 2009, 38(3): 648-663.
    [4] 雷亚国, 贾峰, 孔德同, 等. 大数据下机械智能故障诊断的机遇与挑战[J]. 机械工程学报, 2018, 54(5): 94-104. doi: 10.3901/JME.2018.05.094

    LEI Y G, JIA F, KONG D T, et al. Opportunities and challenges of machinery intelligent fault diagnosis in big data era[J]. Journal of Mechanical Engineering, 2018, 54(5): 94-104(in Chinese). doi: 10.3901/JME.2018.05.094
    [5] WANG S H, XIANG J W, ZHONG Y T, et al. A data indicator-based deep belief networks to detect multiple faults in axial piston pumps[J]. Mechanical Systems and Signal Processing, 2018, 112: 154-170. doi: 10.1016/j.ymssp.2018.04.038
    [6] LIU R N, YANG B Y, ZIO E, et al. Artificial intelligence for fault diagnosis of rotating machinery: A review[J]. Mechanical Systems and Signal Processing, 2018, 108: 33-47. doi: 10.1016/j.ymssp.2018.02.016
    [7] LAN Y, HU J W, HUANG J H, et al. Fault diagnosis on slipper abrasion of axial piston pump based on Extreme Learning Machine[J]. Measurement, 2018, 124: 378-385. doi: 10.1016/j.measurement.2018.03.050
    [8] LU C Q, WANG S P, MAKIS V. Fault severity recognition of aviation piston pump based on feature extraction of EEMD paving and optimized support vector regression model[J]. Aerospace Science and Technology, 2017, 67: 105-117. doi: 10.1016/j.ast.2017.03.039
    [9] 杜振东, 赵建民, 李海平, 等. 基于SA-EMD-PNN的柱塞泵故障诊断方法研究[J]. 振动与冲击, 2019, 38(8): 145-152. doi: 10.13465/j.cnki.jvs.2019.08.022

    DU Z D, ZHAO J M, LI H P, et al. A fault diagnosis method of a plunger pump based on SA-EMD-PNN[J]. Journal of Vibration and Shock, 2019, 38(8): 145-152(in Chinese). doi: 10.13465/j.cnki.jvs.2019.08.022
    [10] 王鹏飞, 王新晴, 高天宇, 等. 基于包络谱和SVM的柱塞泵负荷状态识别[J]. 机械设计与制造, 2015(12): 237-239. doi: 10.3969/j.issn.1001-3997.2015.12.066

    WANG P F, WANG X Q, GAO T Y, et al. The recognition of piston pump’s load condition based on envelop spectrum and SVM[J]. Machinery Design & Manufacture, 2015(12): 237-239(in Chinese). doi: 10.3969/j.issn.1001-3997.2015.12.066
    [11] 汪海晋, 尹宗宇, 柯臻铮, 等. 基于一维卷积神经网络的螺旋铣刀具磨损监测[J]. 浙江大学学报(工学版), 2020, 54(5): 931-939. doi: 10.3785/j.issn.1008-973X.2020.05.010

    WANG H J, YIN Z Y, KE Z Z, et al. Wear monitoring of helical milling tool based on one-dimensional convolutional neural network[J]. Journal of Zhejiang University (Engineering Science), 2020, 54(5): 931-939(in Chinese). doi: 10.3785/j.issn.1008-973X.2020.05.010
    [12] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527
    [13] VOULODIMOS A, DOULAMIS N, DOULAMIS A, et al. Deep learning for computer vision: A brief review[J]. Computational Intelligence and Neuroscience, 2018, 2018: 7068349.
    [14] YOUNG T, HAZARIKA D, PORIA S, et al. Recent trends in deep learning based natural language processing[J]. IEEE Computational Intelligence Magazine, 2018, 13(3): 55-75. doi: 10.1109/MCI.2018.2840738
    [15] TIAN C W, FEI L K, ZHENG W X, et al. Deep learning on image denoising: an overview[J]. Neural Networks, 2020, 131: 251-275. doi: 10.1016/j.neunet.2020.07.025
    [16] HOANG D T, KANG H J. A survey on deep learning based bearing fault diagnosis[J]. Neurocomputing, 2019, 335: 327-335. doi: 10.1016/j.neucom.2018.06.078
    [17] 任浩, 屈剑锋, 柴毅, 等. 深度学习在故障诊断领域中的研究现状与挑战[J]. 控制与决策, 2017, 32(8): 1345-1358. doi: 10.13195/j.kzyjc.2016.1625

    REN H, QU J F, CHAI Y, et al. Deep learning for fault diagnosis: The state of the art and challenge[J]. Control and Decision, 2017, 32(8): 1345-1358(in Chinese). doi: 10.13195/j.kzyjc.2016.1625
    [18] 魏晓良, 潮群, 陶建峰, 等. 基于LSTM和CNN的高速柱塞泵故障诊断[J]. 航空学报, 2021, 42(3): 423876.

    WEI X L, CHAO Q, TAO J F, et al. Cavitation fault diagnosis method for high-speed plunger pumps based on LSTM and CNN[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(3): 423876(in Chinese).
    [19] SUN S W, ZHANG S, JIANG W L, et al. Study on the health condition monitoring method of hydraulic pump based on convolutional neural network[C]//2020 12th International Conference on Measuring Technology and Mechatronics Automation. Piscataway: IEEE Press, 2020: 149-153.
    [20] XU G W, LIU M, JIANG Z F, et al. Online fault diagnosis method based on transfer convolutional neural networks[J]. IEEE Transactions on Instrumentation and Measurement, 2020, 69(2): 509-520. doi: 10.1109/TIM.2019.2902003
    [21] WANG S H, XIANG J W. A minimum entropy deconvolution-enhanced convolutional neural networks for fault diagnosis of axial piston pumps[J]. Soft Computing, 2020, 24(4): 2983-2997. doi: 10.1007/s00500-019-04076-2
    [22] CHICCO D. Siamese neural networks: An overview[J]. Methods in Molecular Biology, 2021, 2190: 73-94.
    [23] 贾智涵. 基于深度神经网络的机械故障诊断技术研究[D]. 北京: 北京邮电大学, 2019: 32-34.

    JIA Z H. A study of mechanical fault diagnosis technology based on deep neural network[D]. Beijing: Beijing University of Posts and Telecommunications, 2019: 32-34 (in Chinese).
    [24] 樊琳, 张惊雷. 联合损失优化孪生网络的行人重识别[J]. 计算机工程与科学, 2020, 42(2): 273-280. doi: 10.3969/j.issn.1007-130X.2020.02.012

    FAN L, ZHANG J L. Person re-identification based on joint loss and Siamese network[J]. Computer Engineering & Science, 2020, 42(2): 273-280(in Chinese). doi: 10.3969/j.issn.1007-130X.2020.02.012
    [25] ZHANG Y C, PARDO B, DUAN Z Y. Siamese style convolutional neural networks for sound search by vocal imitation[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2019, 27(2): 429-441. doi: 10.1109/TASLP.2018.2868428
    [26] AHRABIAN K, BABAALI B. Usage of autoencoders and Siamese networks for online handwritten signature verification[J]. Neural Computing and Applications, 2019, 31(12): 9321-9334. doi: 10.1007/s00521-018-3844-z
    [27] 汤何胜, 李晶, 訚耀保. 轴向柱塞泵滑靴副功率损失特性[J]. 中南大学学报(自然科学版), 2017, 48(2): 361-369.

    TANG H S, LI J, YIN Y B. Power loss characteristics of slipper/swash plate pair in axial piston pump[J]. Journal of Central South University (Science and Technology), 2017, 48(2): 361-369(in Chinese).
    [28] 金列俊, 詹建明, 陈俊华, 等. 基于一维卷积神经网络的钻杆故障诊断[J]. 浙江大学学报(工学版), 2020, 54(3): 467-474. doi: 10.3785/j.issn.1008-973X.2020.03.006

    JIN L J, ZHAN J M, CHEN J H, et al. Drill pipe fault diagnosis method based on one-dimensional convolutional neural network[J]. Journal of Zhejiang University (Engineering Science), 2020, 54(3): 467-474(in Chinese). doi: 10.3785/j.issn.1008-973X.2020.03.006
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出版历程
  • 收稿日期:  2021-04-25
  • 录用日期:  2021-06-13
  • 网络出版日期:  2023-01-16
  • 刊出日期:  2021-06-30

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