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基于MTF-MSMCNN的小样本滚动轴承故障诊断方法

段晓燕 焦孟萱 雷春丽 李建华

段晓燕, 焦孟萱, 雷春丽, 等. 基于MTF-MSMCNN的小样本滚动轴承故障诊断方法[J]. 航空动力学报, 2024, 39(1):20230517 doi: 10.13224/j.cnki.jasp.20230517
引用本文: 段晓燕, 焦孟萱, 雷春丽, 等. 基于MTF-MSMCNN的小样本滚动轴承故障诊断方法[J]. 航空动力学报, 2024, 39(1):20230517 doi: 10.13224/j.cnki.jasp.20230517
DUAN Xiaoyan, JIAO Mengxuan, LEI Chunli, et al. A rolling bearing fault diagnosis method based on MTF-MSMCNN with small sample[J]. Journal of Aerospace Power, 2024, 39(1):20230517 doi: 10.13224/j.cnki.jasp.20230517
Citation: DUAN Xiaoyan, JIAO Mengxuan, LEI Chunli, et al. A rolling bearing fault diagnosis method based on MTF-MSMCNN with small sample[J]. Journal of Aerospace Power, 2024, 39(1):20230517 doi: 10.13224/j.cnki.jasp.20230517

基于MTF-MSMCNN的小样本滚动轴承故障诊断方法

doi: 10.13224/j.cnki.jasp.20230517
基金项目: 国家自然科学基金(51465035); 甘肃省自然科学基金(20JR5RA466); 兰州理工大学红柳一流学科建设项目
详细信息
    作者简介:

    段晓燕(1981-),女,讲师,硕士,主要从事智能故障诊断研究

    通讯作者:

    李建华(1975-),男,教授、博士生导师,博士,主要从事智能信号处理研究。E-mail:li_jh@vip.sina.com

  • 中图分类号: V263.6;TH133.33

A rolling bearing fault diagnosis method based on MTF-MSMCNN with small sample

  • 摘要:

    针对样本数量不足以及工况条件复杂导致故障识别精度低下的问题,提出一种基于马尔科夫转移场与多维监督卷积神经网络(Markov transition field and multidimensional supervised module convolutional neural networks, MTF-MSMCNN)的小样本滚动轴承故障诊断方法。采用MTF编码方式将一维滚动轴承信号转化为二维特征图像,使其保留时间相关性;提出多维监督模块(Multidimensional supervision module, MSM),在空间维度和通道维度监测重要故障特征并自适应赋予权重,提升模型捕捉关键特征的能力;将MSM嵌入到卷积神经网络中,构建出MSMCNN模型;通过试验构建复杂工况条件,将MTF图像输入到所提模型进行故障诊断,并运用两种数据集验证模型有效性。试验结果表明,MTF-MSMCNN在每类故障训练集样本仅有10个且在0 dB噪声污染下故障诊断精度依然可达90%左右,对比其他诊断模型,本文所提方法在小样本、变工况以及噪声干扰条件下具有更高的识别准确率、更强的泛化能力以及抗噪性能。

     

  • 图 1  MTF图像生成过程

    Figure 1.  MTF image generation process

    图 2  CNN结构

    Figure 2.  CNN structure

    图 3  多维监督模块结构

    Figure 3.  Structure of multidimensional supervision module

    图 4  MTF-MSMCNN故障诊断网络模型

    Figure 4.  MTF-MSMCNN fault diagnosis network model

    图 5  美国凯斯西储大学滚动轴承试验台

    Figure 5.  Rolling bearing test rig of Case Western Reserve University

    图 6  MFS滚动轴承试验台

    Figure 6.  MFS rolling bearing test rig

    图 7  ER-16K故障部位

    Figure 7.  Fault location of ER-16K

    图 8  MTF二维图像

    Figure 8.  Two-dimensional image of MTF

    图 9  变负载箱型图

    Figure 9.  Variable load box type diagram

    图 10  不同样本数量下的混淆矩阵

    Figure 10.  Confusion matrix under different samples

    图 11  变转速各模型分类准确率

    Figure 11.  Classification accuracy of models with variable speed

    图 12  不同模型抗噪雷达图

    Figure 12.  Anti-noise radar of different models

    表  1  数据集1

    Table  1.   Data set 1

    工况样本数量
    D=0 mmD=0.18 mmD=0.36 mm
    正常
    (标签0)
    内圈故障
    (标签1)
    外圈故障
    (标签2)
    滚动体故障
    (标签3)
    内圈故障
    (标签4)
    外圈故障
    (标签5)
    滚动体故障
    (标签6)
    工况A
    P=0.745 7 kW)
    训练集40404040404040
    测试集100100100100100100100
    工况B
    P=1.491 4 kW)
    训练集40404040404040
    测试集100100100100100100100
    工况C
    P=2.237 1 kW)
    训练集40404040404040
    测试集100100100100100100100
    下载: 导出CSV

    表  2  数据集2

    Table  2.   Data set 2

    工况样本数量
    D=0 mmD=0.6 mmD=1.2 mm
    正常
    (标签0)
    内圈故障
    (标签1)
    外圈故障
    (标签2)
    滚动体故障
    (标签3)
    内圈故障
    (标签4)
    外圈故障
    (标签5)
    滚动体故障
    (标签6)
    工况D
    n=1 200 r/min)
    训练集40404040404040
    测试集100100100100100100100
    工况E
    n=1 300 r/min)
    训练集40404040404040
    测试集100100100100100100100
    工况F
    n=1 400 r/min)
    训练集40404040404040
    测试集100100100100100100100
    下载: 导出CSV

    表  3  变负载各模型分类准确率

    Table  3.   Classification accuracy of each model under variable load

    样本数量试验方法变负载分类准确率/%
    A-BA-CB-AB-CC-AC-B平均
    10MTF-MSMCNN99.4397.4899.4098.3497.4999.0198.53
    MSCNN98.8596.6698.2997.3397.0598.0597.71
    GADF-MSMCNN75.4361.1473.8162.1960.8666.1066.59
    MC_CNN98.7195.5298.4396.6796.0997.8197.21
    ADCNN98.9197.4398.8097.8797.4798.0198.08
    20MTF-MSMCNN99.8098.0399.7198.4898.4099.2698.95
    MSCNN99.1097.0498.9597.8197.7198.4798.18
    GADF-MSMCNN79.6271.0575.8172.9567.7173.1473.38
    MC_CNN99.0497.5798.9097.8297.1498.7198.20
    ADCNN98.9597.9898.8698.3698.1998.4898.47
    30MTF-MSMCNN99.9198.5799.8998.8998.6399.6699.26
    MSCNN99.3397.5299.2997.9097.8699.0598.49
    GADF-MSMCNN81.5373.3380.0074.7671.5275.6276.13
    MC_CNN93.3398.1099.2198.3397.8699.1497.66
    ADCNN99.3298.3799.2598.8198.5198.9198.86
    40MTF-MSMCNN99.9698.9199.9499.1799.1599.9299.51
    MSCNN99.5297.8199.7198.3298.0999.4398.81
    GADF-MSMCNN83.3374.4581.9077.9972.5783.0978.89
    MC_CNN99.4398.3499.2899.0998.4399.3898.99
    ADCNN99.4898.7699.4898.9599.0599.0999.14
    下载: 导出CSV
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  • 收稿日期:  2023-08-07
  • 网络出版日期:  2023-10-17

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