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一种改进灰狼算法优化LSSVR的混凝土泵车砼活塞剩余寿命预测方法研究

胡锴沣 孟祥印 李召鑫 赖焕杰

胡锴沣,孟祥印,李召鑫, 等. 一种改进灰狼算法优化LSSVR的混凝土泵车砼活塞剩余寿命预测方法研究[J]. 机械科学与技术,2023,42(2):246-251 doi: 10.13433/j.cnki.1003-8728.20200595
引用本文: 胡锴沣,孟祥印,李召鑫, 等. 一种改进灰狼算法优化LSSVR的混凝土泵车砼活塞剩余寿命预测方法研究[J]. 机械科学与技术,2023,42(2):246-251 doi: 10.13433/j.cnki.1003-8728.20200595
HU Kaifeng, MENG Xiangyin, LI Zhaoxin, LAI Huanjie. An Improved Gray Wolf Algorithm to Optimize LSSVR for Residual Life Prediction Method of Concrete Pump Truck Concrete Piston[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(2): 246-251. doi: 10.13433/j.cnki.1003-8728.20200595
Citation: HU Kaifeng, MENG Xiangyin, LI Zhaoxin, LAI Huanjie. An Improved Gray Wolf Algorithm to Optimize LSSVR for Residual Life Prediction Method of Concrete Pump Truck Concrete Piston[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(2): 246-251. doi: 10.13433/j.cnki.1003-8728.20200595

一种改进灰狼算法优化LSSVR的混凝土泵车砼活塞剩余寿命预测方法研究

doi: 10.13433/j.cnki.1003-8728.20200595
详细信息
    作者简介:

    胡锴沣(1998−),硕士研究生,研究方向为数据驱动的机械零部件故障诊断与寿命预测,2627379609@qq.com

    通讯作者:

    孟祥印,副教授,硕士生导师,mxy412@163.com

  • 中图分类号: THl7

An Improved Gray Wolf Algorithm to Optimize LSSVR for Residual Life Prediction Method of Concrete Pump Truck Concrete Piston

  • 摘要: 为了解决混凝土泵车砼活塞因无法及时更换导致设备停机的问题,提出一种改进灰狼算法优化最小二乘支持向量回归(LSSVR)的剩余寿命预测方法,该方法使用差分算法(DE)优化原始灰狼算法(GWO),解决了其容易陷入局部最优解的问题,提高了收敛速度,使用优化后的算法优化最小二乘支持向量回归的两个参数,建立剩余寿命预测模型。通过真实的砼活塞寿命监测数据,使用3种评估指标对比LSSVR、GWO-LSSVR、DE-GWO-LSSVR这3个模型的预测效果,并与相关研究的结果进行对比。实验表明,DE-GWO-LSSVR模型拥有最高的预测精度,可以为砼活塞的预测性更换以及机械零件的故障诊断提供指导意义。
  • 图  1  DE-GWO-LSSVR回归预测算法流程图

    图  2  参数C迭代曲线图

    图  3  参数$ \sigma $迭代曲线图

    图  4  LSSVR模型预测结果

    图  5  GWO-LSSVR模型预测结果

    图  6  DE-GWO-LSSVR模型预测结果

    表  1  状态监测数据详情表

    数据类型数据名称
    连续型累积工作时长、油缸压力、混凝土压力、
    温度、流量、电机转速、电流
    离散型设备型号、报警信号、开关信号
    下载: 导出CSV

    表  2  各模型预测误差指标

    预测模型 MREMAEMSE
    LSSVR0.06360.1011 264.90
    GWO-LSSVR0.05249.649 961.10
    DE-GWO-LSSVR0.04744.838 373.15
    下载: 导出CSV

    表  3  效果对比表

    预测模型MRE
    Lightgbm模型 0.060
    BP神经网络模型 0.090
    SVR模型 0.089
    ensemble模型[15] 0.049
    DE-GWO-LSSVR 0.047
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-03-23
  • 网络出版日期:  2023-03-27
  • 刊出日期:  2023-02-25

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