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DBN与PSO-SVM的滚动轴承故障诊断

熊景鸣 潘林 朱昇 孟宗

熊景鸣, 潘林, 朱昇, 孟宗. DBN与PSO-SVM的滚动轴承故障诊断[J]. 机械科学与技术, 2019, 38(11): 1726-1731. doi: 10.13433/j.cnki.1003-8728.20190040
引用本文: 熊景鸣, 潘林, 朱昇, 孟宗. DBN与PSO-SVM的滚动轴承故障诊断[J]. 机械科学与技术, 2019, 38(11): 1726-1731. doi: 10.13433/j.cnki.1003-8728.20190040
Xiong Jingming, Pan Lin, Zhu Sheng, Meng Zong. Bearing Fault Diagnosis based on Deep Belief Networks and Particle Swarm Optimization Support Vector Machine[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(11): 1726-1731. doi: 10.13433/j.cnki.1003-8728.20190040
Citation: Xiong Jingming, Pan Lin, Zhu Sheng, Meng Zong. Bearing Fault Diagnosis based on Deep Belief Networks and Particle Swarm Optimization Support Vector Machine[J]. Mechanical Science and Technology for Aerospace Engineering, 2019, 38(11): 1726-1731. doi: 10.13433/j.cnki.1003-8728.20190040

DBN与PSO-SVM的滚动轴承故障诊断

doi: 10.13433/j.cnki.1003-8728.20190040
基金项目: 

铜仁市科技计划项目 铜市科研[2017]25号

河北省高等学校科学研究计划重点项目 ZD2015049

详细信息
    作者简介:

    熊景鸣(1993-), 讲师, 硕士, 研究方向为机械动力学分析, xjm_master@stumail.ysu.edu.cn

  • 中图分类号: TH133.33

Bearing Fault Diagnosis based on Deep Belief Networks and Particle Swarm Optimization Support Vector Machine

  • 摘要: 针对如何提高轴承故障诊断的准确率和算法训练的效率问题,提出了一种深度信念网络(DBN)与粒子群优化支持向量机(PSO-SVM)的滚动轴承故障诊断方法。首先,求出信号的时频特征统计量,其次,利用DBN对时频特征统计量进行特征提取,最后,利用PSO-SVM进行分类。实验结果表明:相比于直接用PSO-SVM进行分类,该方法不仅准确率更高,而且算法训练的时间大大缩短了,提高了滚动轴承故障诊断的准确率和效率。
  • 图  1  DBN模型

    图  2  三维图

    图  3  粒子群适宜度曲线

    表  1  时域特征参量

    特征参数 表达式 注意
    平均值
    有效值 xi是信号x的第i个值, N是数据总数
    波形因子
    峰峰值 Pk=max[x]-min[x]
    波峰因子
    峭度 σ2为方差
    峭度因子
    脉冲因子 I=PK/(av) av为绝对值的平均
    Xr Xr=mean{sqrt[abs(X)]}2
    裕度因子 L=PK/xr
    St
    下载: 导出CSV

    表  2  频域特征参量

    特征参数 表达式 注意
    频度重心
    RMS变异频率
    Root变异频率
    下载: 导出CSV

    表  3  轴承数据集

    故障类型 训练/测试样本 数据集 类别编号
    正常 100/100 97DE 000
    内圈故障(0.177 8) 100/100 105DE 001
    内圈故障(0.355 6) 100/100 169DE 010
    内圈故障(0.533 4) 100/100 211DE 011
    外圈故障(0.177 8) 100/100 130DE 100
    外圈故障(0.355 6) 100/100 198DE 101
    外圈故障(0.533 4) 100/100 236DE 110
    下载: 导出CSV

    表  4  分类器性能对照表

    方法 运行时间/s (c, g) 准确率/%
    PSO-SVM 27.36 (7.093 1, 5.239 0) 84.85
    DBN-PSO-SVM 6.62 (158.953 5, 31.818 3) 97.34
    下载: 导出CSV
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出版历程
  • 收稿日期:  2018-01-05
  • 刊出日期:  2019-11-05

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