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基于改进相似模型的采煤机轴承剩余寿命预测方法

李晓昆 耿毅德 王宏伟 付翔 王然风

李晓昆,耿毅德,王宏伟,等. 基于改进相似模型的采煤机轴承剩余寿命预测方法[J]. 工矿自动化,2023,49(5):96-103.  doi: 10.13272/j.issn.1671-251x.18018
引用本文: 李晓昆,耿毅德,王宏伟,等. 基于改进相似模型的采煤机轴承剩余寿命预测方法[J]. 工矿自动化,2023,49(5):96-103.  doi: 10.13272/j.issn.1671-251x.18018
LI Xiaokun, GENG Yide, WANG Hongwei, et al. A method for predicting the remaining useful life of shearer bearings based on improved similarity model[J]. Journal of Mine Automation,2023,49(5):96-103.  doi: 10.13272/j.issn.1671-251x.18018
Citation: LI Xiaokun, GENG Yide, WANG Hongwei, et al. A method for predicting the remaining useful life of shearer bearings based on improved similarity model[J]. Journal of Mine Automation,2023,49(5):96-103.  doi: 10.13272/j.issn.1671-251x.18018

基于改进相似模型的采煤机轴承剩余寿命预测方法

doi: 10.13272/j.issn.1671-251x.18018
基金项目: 国家自然科学基金项目(52274157);“科技兴蒙”行动重点专项项目(2022EEDSKJXM010);山西省基础研究计划项目(202103021223123);山西省重点研发计划项目(202102100401015);山西省揭榜招标项目(20201101005)。
详细信息
    作者简介:

    李晓昆(1998—),男,河北承德人,硕士研究生,主要研究方向为煤矿设备故障诊断与预测性维护研究,E-mail:1615098598@qq.com

    通讯作者:

    王然风(1970—),男,山西长治人,副教授,博士,主要研究方向为智能化开采与分选,E-mail:wrf197010@126.com

  • 中图分类号: TD67

A method for predicting the remaining useful life of shearer bearings based on improved similarity model

  • 摘要: 采煤机轴承退化过程并非简单的线性或指数关系,应分为不同阶段进行分析。而目前的采煤机轴承剩余使用寿命(RUL)预测方法未充分考虑该因素。针对该问题,提出了一种基于改进相似模型的采煤机轴承剩余寿命预测方法。采用通用的相似模型描述设备退化过程,在此基础上通过对均方根聚类分析,将轴承退化过程划分为平稳运行阶段、初始退化阶段和加速退化阶段,借助传统相似模型思路分段计算采煤机轴承的健康状态并拟合得到退化曲线样本库,通过对离线样本库数据和在线采煤机实时数据进行数据预处理和相似性分析,最终得到采煤机轴承RUL。实验结果表明:基于改进相似模型的采煤机轴承RUL预测方法的误差绝对值均值较卷积门控循环单元(ConvGRU)、空间卷积长短时记忆神经网络(ConvLSTM)、卷积神经网络(CNN)、自组织映射神经网络(SOM)、循环神经网络(RNN)、传统相似模型分别降低了30.49%,7.54%,16.98%,24.74%,17.96%,9.49%,可以较好地预测轴承RUL。现场试验结果表明:对采煤机轴承连续监测87 d,轴承健康状态从0.997逐渐下降到0.972,与现场采煤机轴承实际使用情况基本吻合,验证了该方法的有效性。

     

  • 图  1  采煤机轴承RUL预测框架

    Figure  1.  Remaining useful life prediction framework of shearer bearing

    图  2  基于改进相似模型的采煤机轴承RUL预测流程

    Figure  2.  Remaining useful life prediction process of shearer bearing based on improved similarity model

    图  3  基于改进相似模型的采煤机轴承RUL预测方法

    Figure  3.  Remaining useful life prediction method of shearer bearing based on improved similarity model

    图  4  HS划分结果

    Figure  4.  Health Stage division results

    图  5  退化曲线族

    Figure  5.  Degradation curve family

    图  6  井下传感器布置点位

    Figure  6.  Arrangement points of underground sensors

    图  7  采煤机轴承振动信号

    Figure  7.  Vibration signal of shearer bearing

    图  8  采煤机轴承振动信号频谱

    Figure  8.  Spectrum of vibration signal of shearer bearing

    图  9  采煤机轴承退化曲线

    Figure  9.  Degradation curve of shearer bearing

    表  1  IMS Center 的滚动轴承全生命周期数据集

    Table  1.   Rolling bearing life cycle data set from IMS Center

    试验编号测量方向样本数量样本长度运行时间/d试验描述
    试验AX/Y82 155343号轴承内圈故障
    4号轴承滚动体故障
    试验BX498481号轴承外圈故障
    试验CX46 324513号轴承外圈故障
    下载: 导出CSV

    表  2  可识别性系数归一化结果

    Table  2.   Normalization results of recognizability coefficient

    健康指标可识别性系数归一化健康指标可识别性系数归一化
    峭度0.694 3峰值0.450 7
    脉冲指标0.475 3RMS1
    波形0.853 2最大值0.632 6
    裕度0.417 9最小值0.189 8
    歪度0.147 4平均值0
    下载: 导出CSV

    表  3  不同预测方法对比

    Table  3.   Comparison of different prediction methods %

    方法不同测试集预测误差绝对值误差绝对值
    均值
    轴承1−3轴承1−4轴承1−6轴承1−7轴承2−5轴承2−6
    本文方法22.2819.5716.4313.6721.2317.3918.43
    ConvGRU89.5344.2129.1734.8760.8434.8848.92
    ConvLSTM33.6847.2423.283.3039.808.5225.97
    CNN−HI48.5253.5719.3916.2756.1318.6535.41
    SOM−HI31.7662.7632.8811.0968.6151.9443.17
    RNN−HI43.2867.5521.3317.8354.3713.9536.39
    相似模型方法30.5236.6821.6119.6532.1326.9527.92
    下载: 导出CSV

    表  4  采煤机轴承HC与运行时间关系

    Table  4.   Relationship between health condition and operation time of shearer bearing

    运行时间/d122446687
    采煤机轴承HC0.9970.9920.9840.9790.972
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-11-22
  • 修回日期:  2023-02-20
  • 网络出版日期:  2023-05-09

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