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基于改进宽度模型迁移学习的不同负载下滚动轴承状态快速分类方法

康守强 杨佳轩 王玉静 王庆岩 梁欣涛 MIKULOVICH V I

康守强, 杨佳轩, 王玉静, 王庆岩, 梁欣涛, MIKULOVICH V I. 基于改进宽度模型迁移学习的不同负载下滚动轴承状态快速分类方法[J]. 电子与信息学报, 2023, 45(5): 1824-1832. doi: 10.11999/JEIT220401
引用本文: 康守强, 杨佳轩, 王玉静, 王庆岩, 梁欣涛, MIKULOVICH V I. 基于改进宽度模型迁移学习的不同负载下滚动轴承状态快速分类方法[J]. 电子与信息学报, 2023, 45(5): 1824-1832. doi: 10.11999/JEIT220401
KANG Shouqiang, YANG Jiaxuan, WANG Yujing, WANG Qingyan, LIANG Xintao, . A Fast Classification Method of Rolling Bearing State Under Different Loads Based on Improved Broad Model Transfer Learning[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1824-1832. doi: 10.11999/JEIT220401
Citation: KANG Shouqiang, YANG Jiaxuan, WANG Yujing, WANG Qingyan, LIANG Xintao, . A Fast Classification Method of Rolling Bearing State Under Different Loads Based on Improved Broad Model Transfer Learning[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1824-1832. doi: 10.11999/JEIT220401

基于改进宽度模型迁移学习的不同负载下滚动轴承状态快速分类方法

doi: 10.11999/JEIT220401
基金项目: 国家自然科学基金(51805120),黑龙江省自然科学基金(LH2019E058)
详细信息
    作者简介:

    康守强:男,教授,研究方向为非平稳信号处理、故障诊断、状态评估与预测技术

    杨佳轩:女,硕士生,研究方向为振动信号处理、故障诊断技术

    王玉静:女,教授,研究方向为非平稳信号处理、故障诊断、状态评估与预测技术

    王庆岩:男,讲师,研究方向为信号处理、遥感图像智能解译、模式识别

    梁欣涛:男,讲师,研究方向为非平稳信号处理、语音信号处理

    MIKULOVICH V I:男,教授,研究方向为非平稳信号处理、故障诊断、状态评估与预测技术

    通讯作者:

    王玉静 mirrorwyj@163.com

  • 中图分类号: TN911.7; TH165.3

A Fast Classification Method of Rolling Bearing State Under Different Loads Based on Improved Broad Model Transfer Learning

Funds: The National Natural Science Foundation of China (51805120), The Natural Science Foundation of Heilongjiang Province (LH2019E058)
  • 摘要: 针对深度学习网络训练耗时以及不同负载下滚动轴承的源域数据和目标域数据分布差异较大的问题,该文提出一种基于改进宽度模型迁移学习的滚动轴承状态快速分类方法。该方法首先对不同负载下滚动轴承振动信号进行快速傅里叶变换,构建频域幅值序列数据集,并选取某种或某些负载数据集作为源域,其他负载数据集作为目标域;其次以循环扩展的方式建立宽度学习系统(BLS)的增强节点窗口,并在增强层引入Maxout激活函数构建改进的BLS网络,同时引入遗传算法优化网络节点结构,建立基于源域数据的预训练模型;最后将预训练模型的网络参数、特征层和增强层的权重参数迁移至目标域网络,并利用少量目标域样本微调网络建立状态分类模型。实验结果表明,所提方法平均训练时间为32.6 s,平均测试准确率为98.9%。对比其他方法,所提方法可以在更短的时间内建立分类模型并获得良好的分类准确率。
  • 图  1  BLS网络的结构示意图

    图  2  改进BLS网络生成增强特征的过程示意图

    图  3  基于改进宽度学习网络的模型迁移策略

    图  4  基于改进宽度模型迁移学习的滚动轴承状态快速分类方法流程框图

    图  5  实验装置示意图

    图  6  网络参数优化前后对比实验结果的可视化

    图  7  所提方法与其他方法对比的实验结果

    图  8  迁移实验结果 (MFPT数据库)

    图  9  所提方法改进前后对比实验结果 (MFPT数据库)

    算法1 改进BLS网络获得增强特征算法
     输入:映射特征Z
     输出:增强特征H
     (1) for n = 1 to N4 //n表示增强节点窗口数目,N4表示增强节
     点窗口的最大数目
     (2)  随机生成${W_{{h_k}}}$并对${W_{{h_k}}}$进行正交规范化
     (3)  OutofEachWindow←np.dot(Z×${{\boldsymbol{W}}_{{h_k}}}$+$ {{\boldsymbol{\beta}} _{{h_k}}} $) //$ {{\boldsymbol{\beta}} _{{h_k}}} $表示偏
     置,随机生成
     (4)  OutofAllWindow[:,nn+1]←np.max
     (OutofEachWindow, axis=1)
     (5) end for
     (6) H←OutofAllWindow
    下载: 导出CSV

    表  1  迁移任务构成

    迁移任务源域样本集负载 (kW)目标域样本集负载 (kW)源域样本数 (个)目标域样本数 (个)总样本数 (个)
    2_31.502.256006001200
    2_131.501.50, 2.2560012001800
    3_0122.250, 0.75, 1.5060018002400
    13_20.75, 2.251.5012006001800
    02_130, 1.500.75, 2.25120012002400
    023_10, 1.50, 2.250.7518006002400
    下载: 导出CSV

    表  2  BLS网络改进前后实验结果

    网络模型平均测试准确率 (%)平均训练时间 (s)
    基于BLS网络的迁移模型97.027.4
    基于改进BLS网络的
    迁移模型
    98.435.1
    下载: 导出CSV

    表  3  随机设置的5组网络参数

    组数N1N2N3N4
    1513010
    23040520
    350204030
    42051040
    510302050
    下载: 导出CSV

    表  4  模型迁移前后实验结果

    是否迁移平均测试准确率 (%)平均训练时间 (s)
    98.435.1
    90.129.1
    下载: 导出CSV

    表  5  迁移任务构成 (MFPT数据库)

    迁移任务源域样本集目标域样本集源域样本数 (个)目标域样本数 (个)总样本数 (个)
    A_CAC180180360
    A_CDAC D180360540
    D_ABCDA B C180540720
    CD_BC DB360180540
    AB_CDA BC D360360720
    ACD_BA C DB540180720
    下载: 导出CSV

    表  6  所提方法与其他方法的对比实验结果 (MFPT数据库)

    方法平均训练时间 (s)平均测试准确率 (%)
    CNN71.690.8
    LSTM57.890.0
    BPNN135.193.7
    DBN[17]565.789.7
    文献[18]1076.499.1
    文献[19]105.6100.0
    所提方法30.099.4
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-06
  • 修回日期:  2022-09-06
  • 录用日期:  2022-09-06
  • 网络出版日期:  2022-09-09
  • 刊出日期:  2023-05-10

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