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智能化滚珠丝杠副退化状态的评估方法

张江泉 高宏力 向守兵 郭亮 谭咏文

张江泉, 高宏力, 向守兵, 郭亮, 谭咏文. 智能化滚珠丝杠副退化状态的评估方法[J]. 西南交通大学学报, 2022, 57(4): 813-820. doi: 10.3969/j.issn.0258-2724.20220082
引用本文: 张江泉, 高宏力, 向守兵, 郭亮, 谭咏文. 智能化滚珠丝杠副退化状态的评估方法[J]. 西南交通大学学报, 2022, 57(4): 813-820. doi: 10.3969/j.issn.0258-2724.20220082
ZHANG Jiangquan, GAO Hongli, XIANG Shoubing, GUO Liang, TAN Yongwen. Intelligent Evaluation Method for Ball Screw Degradation State[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 813-820. doi: 10.3969/j.issn.0258-2724.20220082
Citation: ZHANG Jiangquan, GAO Hongli, XIANG Shoubing, GUO Liang, TAN Yongwen. Intelligent Evaluation Method for Ball Screw Degradation State[J]. Journal of Southwest Jiaotong University, 2022, 57(4): 813-820. doi: 10.3969/j.issn.0258-2724.20220082

智能化滚珠丝杠副退化状态的评估方法

doi: 10.3969/j.issn.0258-2724.20220082
基金项目: 国家自然科学基金(51775452)
详细信息
    作者简介:

    张江泉(1982—),男,讲师,研究方向为机器学习和机械状态监测技术,E-mail:zhangjiangquan@swjtu.edu.cn

    通讯作者:

    高宏力(1971—),男,教授,研究方向为设备智能化状态监测与故障诊断技术,E-mail:hongli_gao@swjtu.edu.cn

  • 中图分类号: TP277;TH117.1

Intelligent Evaluation Method for Ball Screw Degradation State

  • 摘要:

    现有滚珠丝杠副退化状态评估方法通常假设已有充足且带标签的数据集,但实际工程应用中故障成本过高、获取标签难度过大,难以在特定工况下获得大量带标签数据集. 针对上述问题,提出一种基于多尺度对抗域对抗学习的智能化状态评估方法,结合注意力卷积神经网络模块和域对抗学习模块,利用不同工况下采集的传感器信号建立深度学习模型,从而自适应地学习域不变特征并实现高效的知识复用和特征迁移;利用多工况下采集的滚珠丝杠副退化信号构建试验数据集来验证方法的有效性. 研究结果表明:本文方法在6个标签缺失跨工况条件下的滚珠丝杠副退化状态识别子任务中均取得了高于89.02%的识别准确率;能够充分迁移带标签数据的关键特征,实现了标签样本缺失条件下目标工况退化状态识别.

     

  • 图 1  深度域对抗模型流程

    Figure 1.  Flowchart of deep adversarial training model

    图 2  MACNN模块示意

    Figure 2.  Structure of the MACNN module

    图 3  带注意力机制的CNN网络

    Figure 3.  CNN networks with attention mechanisms

    图 4  SENet的具体实现流程

    Figure 4.  Implementation details of the SENet network

    图 5  多尺度域对抗学习框架

    Figure 5.  Structure of multi-scale adversarial domain adversarial learning

    图 6  滚珠丝杠副加速退化试验台

    Figure 6.  Accelerated degradation test bench for ball screw

    图 7  滚珠丝杠副运行末期信号

    Figure 7.  Signals at the end of the ball screw operation

    图 8  滚珠丝杠副退化状态识别子任务结果

    Figure 8.  Results of ball screw degradation state recognition tasks

    图 9  不同样本数量下的退化状态识别结果

    Figure 9.  Results of ball screw degradation state recognition tasks with various samples quantities

    图 10  t-SNE特征可视化结果

    Figure 10.  Feature visualization results via t-SNE

    表  1  MACNN模块基础参数

    Table  1.   Basic parameters of the MACNN modules

    模块名卷积核大小卷积核个数
    MACNN 13 × 1/5 × 1/16 × 11 × 3
    MACNN 23 × 1/5 × 1/16 × 110 × 3
    MACNN 33 × 1/5 × 1/16 × 110 × 3
    MACNN 43 × 1/5 × 1/16 × 110 × 3
    MACNN 53 × 1/5 × 1/16 × 110 × 3
    下载: 导出CSV

    表  2  所选FFZD4010-3型滚珠丝杠副基础参数

    Table  2.   Basic parameters of selected FFZD4010-3 ball screw

    参数取值参数取值
    公称直径/mm 40 丝杠底径/mm 34.3
    导程/mm 10 循环总圈数/圈 3
    丝杠外径/mm 39.5 额定动载荷/kN 30
    滚珠直径/mm 7.14 额定静载荷/kN 66.3
    下载: 导出CSV

    表  3  试验工况详细参数

    Table  3.   Detailed parameters of test working conditions

    工况轴向载荷/kN丝杠转速/(r•min−1
    10100
    21300
    32800
    下载: 导出CSV

    表  4  滚珠丝杠副退化状态识别试验

    Table  4.   Ball screw degradation state identification test

    名称详情名称详情
    任务 A 工况 1→工况 2 任务 D 工况 2→工况 3
    任务 B 工况 1→工况 3 任务 E 工况 3→工况 1
    任务 C 工况 2→工况 1 任务 F 工况 3→工况 2
    下载: 导出CSV

    表  5  退化状态识别试验结果

    Table  5.   Results of degradation state identification tests %

    名称识别精度名称识别精度
    任务 A 90.76 任务 D 90.51
    任务 B 89.75 任务 E 91.17
    任务 C 92.13 任务 F 89.02
    下载: 导出CSV

    表  6  所提方法与对比方法的结果比较

    Table  6.   Comparison between the proposed method and the state-of-the-art methods

    名称识别精度/%精度标准差
    CNN 73.57 5.33
    JDAN 86.24 0.70
    DANN 87.26 1.47
    本文所提方法 91.84 1.28
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-02-06
  • 修回日期:  2022-05-06
  • 刊出日期:  2022-05-11

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