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一种1D-CNN与多传感器信息融合的液压系统故障诊断方法

陈书辉 章猛 刘辉 张超勇

陈书辉,章猛,刘辉, 等. 一种1D-CNN与多传感器信息融合的液压系统故障诊断方法[J]. 机械科学与技术,2023,42(5):715-723 doi: 10.13433/j.cnki.1003-8728.20220028
引用本文: 陈书辉,章猛,刘辉, 等. 一种1D-CNN与多传感器信息融合的液压系统故障诊断方法[J]. 机械科学与技术,2023,42(5):715-723 doi: 10.13433/j.cnki.1003-8728.20220028
CHEN Shuhui, ZHANG Meng, LIU Hui, ZHANG Chaoyong. A Method for Diagnosing Faults of Hydraulic Pump and Accumulator with 1D-CNN and Multi-sensor Information Fusion[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 715-723. doi: 10.13433/j.cnki.1003-8728.20220028
Citation: CHEN Shuhui, ZHANG Meng, LIU Hui, ZHANG Chaoyong. A Method for Diagnosing Faults of Hydraulic Pump and Accumulator with 1D-CNN and Multi-sensor Information Fusion[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 715-723. doi: 10.13433/j.cnki.1003-8728.20220028

一种1D-CNN与多传感器信息融合的液压系统故障诊断方法

doi: 10.13433/j.cnki.1003-8728.20220028
基金项目: 广东省重点领域研发计划项目(2019B090921001)
详细信息
    作者简介:

    陈书辉(1997−),硕士研究生,研究方向为故障诊断、能耗建模,2096630320@qq.com

    通讯作者:

    张超勇,教授,博士生导师,zcyhust@hust.edu.cn

  • 中图分类号: TH137

A Method for Diagnosing Faults of Hydraulic Pump and Accumulator with 1D-CNN and Multi-sensor Information Fusion

  • 摘要: 针对液压信号复杂且难以诊断的难点,提出一种多尺度一维卷积神经网络与多传感器信息融合的深度神经网络模型(MS1D-CNN-MSIF)对液压泵与蓄能器进行故障诊断。在提出方法中,采用不同大小的卷积核对故障信号进行多尺度特征提取;然后使用多传感器信息融合策略将多个传感器的特征信号进行融合,最后使用Softmax进行分类识别。诊断蓄能器压力状态与液压泵泄漏状态的实验结果表明,与支持向量机、堆栈自编码、深度置信网络比较,提出模型具有更好的故障诊断性能,蓄能器识别精度可达99.50%,液压泵识别精度可达99.73%。
  • 图  1  1D-CNN结构图

    图  2  MS1D-CNN模型结构图

    图  3  MS1D-CNN-MSIF模型结构图

    图  4  液压试验台示意图

    图  5  算法流程图

    图  6  蓄能器诊断实验训练过程中模型的损失值与准确率

    图  7  蓄能器诊断混淆矩阵

    图  8  t-SNE降维可视化

    图  9  液压泵诊断实验训练过程中模型的损失值与准确率

    图  10  液压泵诊断混淆矩阵

    图  11  t-SNE降维可视化

    表  1  蓄能器样本数据

    蓄能器
    状态
    样本总
    数量
    测试样本
    数量
    原始状态
    标签
    故障
    标签
    独热
    编码
    最佳压力35010013030001
    轻微减压35010011520010
    严重减压35010010010100
    接近失效3501009001000
    下载: 导出CSV

    表  2  蓄能器章台识别结果对比

    传感器数据源模型平均识别准确率/%模型平均识别准确率/%
    PS1 1D-CNN 96.30 MS1D-CNN 98.50
    PS2 1D-CNN 88.52 MS1D-CNN 91.67
    PS3 1D-CNN 84.62 MS1D-CNN 90.55
    PS1、PS2、PS3 ELM 84.77 SSAE 98.30
    PS1、PS2、PS3 SVM 93.25 MS1D-CNN-MSIF 99.20
    下载: 导出CSV

    表  3  液压泵样本数据

    液压泵
    状态
    样本总
    数量
    测试样本
    数量
    原始状态
    标签
    故障
    标签
    独热
    编码
    无泄漏 450 125 0 0 100
    轻微泄漏 450 125 1 1 010
    严重泄漏 450 125 2 2 001
    下载: 导出CSV

    表  4  液压泵状态识别结果对比

    传感器数据源模型平均识别准确率/%模型平均识别准确率/%
    FS1 1D-CNN 96.43 MS1D-CNN 99.35
    PS2 1D-CNN 80.50 MS1D-CNN 81.36
    PS3 1D-CNN 88.29 MS1D-CNN 91.17
    PS1、PS2、PS3、FS1 ELM 87.65 SVM 91.50
    PS1、PS2、PS3、FS1 SSAE 97.41 DBN 98.77
    PS2、PS3、FS1 MS1D-CNN-MSIF 99.79
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-05-24
  • 网络出版日期:  2023-05-29
  • 刊出日期:  2023-05-25

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