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托辊非接触式故障识别方法研究

郝洪涛 苏耀瑞 丁文捷 冯宝忠

郝洪涛,苏耀瑞,丁文捷, 等. 托辊非接触式故障识别方法研究[J]. 机械科学与技术,2023,42(5):665-672 doi: 10.13433/j.cnki.1003-8728.20220008
引用本文: 郝洪涛,苏耀瑞,丁文捷, 等. 托辊非接触式故障识别方法研究[J]. 机械科学与技术,2023,42(5):665-672 doi: 10.13433/j.cnki.1003-8728.20220008
HAO Hongtao, SU Yaorui, DING Wenjie, FENG Baozhong. Study on Non-contact Identification Method of Idler Faults[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 665-672. doi: 10.13433/j.cnki.1003-8728.20220008
Citation: HAO Hongtao, SU Yaorui, DING Wenjie, FENG Baozhong. Study on Non-contact Identification Method of Idler Faults[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(5): 665-672. doi: 10.13433/j.cnki.1003-8728.20220008

托辊非接触式故障识别方法研究

doi: 10.13433/j.cnki.1003-8728.20220008
基金项目: 宁夏回族自治区重点研发项目(2019BDE03001)与宁夏自然科学基金项目(2021AAC03046)
详细信息
    作者简介:

    郝洪涛(1976−),博士,研究方向为机电设备健康监测,haoht_03@126.com

  • 中图分类号: TH17

Study on Non-contact Identification Method of Idler Faults

  • 摘要: 因托辊故障引发的远程带式输送机事故越来越多,而传统的人工巡检已不能满足需求,且现有接触式加速度信号检测方式存在传感器需求量大及数据收集难的问题,所以有必要通过智能巡检机器人搭载拾音器进行非接触式巡检。托辊运行环境嘈杂,为剔除信号中的噪声,提出基于完全噪声辅助集合经验模态分解(CEEMDAN)、主成分分析(PCA)和鲁棒性独立分量分析(RobustICA)的单通道盲源分离(SCBSS)去噪方法;托辊信号具有非平稳、非线性的特点,仅用梅尔倒谱系数(MFCC)不能完美刻画信号特征参数,提出基于CEEMDAN、PCA、MFCC、MFCC的1阶差分系数和Delta值的自适应特征参数提取方法;最后采用支持向量机(SVM)作为分类器进行故障识别,识别率达到97.2%。
  • 图  1  CEEMDAN-PCA-RobustICA算法示意图

    图  2  MFCC算法示意图

    图  3  CEEMDAN-PCA-MFCC算法示意图

    图  4  托辊信号模拟采集示意图

    图  5  信号时域波形

    图  6  剩余IMF分量所占比重

    图  7  分离信号时域波形

    图  8  正常轴承前四维特征值

    图  9  不同噪声环境下的识别结果

    表  1  各分离信号与源信号相关性值

    分离信号正常滚动体故障内圈故障外圈故障
    ${s_1}$−0.150.230.390.03
    ${s_2}$0.42−0.580.25−0.46
    ${s_3}$−0.04−0.260.020.27
    ${s_4}$−0.760.010.280.09
    ${s_5}$0.400.150.78−0.41
    ${s_6}$0.060.690.020.63
    下载: 导出CSV

    表  2  特征提取方案

    特征参数提取方法简记
    MFCC MFCC
    EMD + MFCC + 差分系数 E + MFCC + 差分
    CEEMDAN + MFCC + 差分系数 C + MFCC + 差分
    SCBSS + CEEMDAN + MFCC + 差分系数 B + C + MFCC + 差分
    下载: 导出CSV

    表  3  不同特征提取方法的识别结果

    特征参数故障
    类型
    识别
    率/%
    整体识
    别率/%
    训练时
    间/min
    MFCC 正常 91.5 71.4 19.8
    滚动体故障 51.0
    内圈故障 45.4
    外圈故障 97.8
    E + MFCC + 差分 正常 61.2 72.6 19.9
    滚动体故障 62.8
    内圈故障 69.7
    外圈故障 96.8
    C + MFCC + 差分 正常 72.6 90 20 .0
    滚动体故障 71.8
    内圈故障 72.8
    外圈故障 98.2
    B + C + MFCC + 差分 正常 98.2 97.2 19.9
    滚动体故障 95.0
    内圈故障 95.6
    外圈故障 99.9
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-09
  • 网络出版日期:  2023-05-29
  • 刊出日期:  2023-05-25

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