• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus 收录
  • 全国中文核心期刊
  • 中国科技论文统计源期刊
  • 中国科学引文数据库来源期刊

监测数据驱动的城轨列车轴箱轴承剩余寿命预测

王彪 秦勇 贾利民 程晓卿 曾春平 高一凡

王彪, 秦勇, 贾利民, 程晓卿, 曾春平, 高一凡. 监测数据驱动的城轨列车轴箱轴承剩余寿命预测[J]. 西南交通大学学报, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230
引用本文: 王彪, 秦勇, 贾利民, 程晓卿, 曾春平, 高一凡. 监测数据驱动的城轨列车轴箱轴承剩余寿命预测[J]. 西南交通大学学报, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230
WANG Biao, QIN Yong, JIA Limin, CHENG Xiaoqing, ZENG Chunping, GAO Yifan. Monitoring Data-Driven Prediction of Remaining Useful Life of Axle-Box Bearings for Urban Rail Transit Trains[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230
Citation: WANG Biao, QIN Yong, JIA Limin, CHENG Xiaoqing, ZENG Chunping, GAO Yifan. Monitoring Data-Driven Prediction of Remaining Useful Life of Axle-Box Bearings for Urban Rail Transit Trains[J]. Journal of Southwest Jiaotong University, 2024, 59(1): 229-238. doi: 10.3969/j.issn.0258-2724.20220230

监测数据驱动的城轨列车轴箱轴承剩余寿命预测

doi: 10.3969/j.issn.0258-2724.20220230
基金项目: 国家自然科学基金(61833002-3)
详细信息
    作者简介:

    王彪(1992—),男,讲师,博士,研究方向为智能剩余寿命预测与健康管理,E-mail: wbiao@bjtu.edu.cn

    通讯作者:

    秦勇(1971—),男,教授,博士,研究方向为交通控制与安全,E-mail: yqin@bjtu.edu.cn

  • 中图分类号: U264.8

Monitoring Data-Driven Prediction of Remaining Useful Life of Axle-Box Bearings for Urban Rail Transit Trains

  • 摘要:

    城轨列车轴箱轴承的运行工况复杂多变、外部随机干扰频繁,导致其监测数据中包含大量测量噪声乃至“脏”数据,进而制约了剩余寿命预测模型的精度. 为解决上述问题,提出了一种监测数据驱动的动态多重聚合剩余寿命预测方法. 首先,通过度量短时数据的幅值分布相似性自动识别并清洗“脏”数据;然后,将健康指标按不同时间尺度进行动态聚合,预测出各类潜在的未来退化轨迹,进而获得轴箱轴承的剩余寿命预测均值与方差;并使用现场实测数据与加速寿命实验数据对提出方法进行验证. 结果表明:所提方法能有效剔除监测数据中的空采数据和强干扰数据;剩余寿命预测均值随累计行驶里程的增加逐渐收敛到真实值,且95%置信区间越来越窄;相比于单指数预测模型和混合预测模型,提出方法的累计相对精度平均值分别提高了29.78%和27.63%,预测收敛速度平均值分别增加了10.56%和10.20%.

     

  • 图 1  可用监测数据与异常监测数据示例

    Figure 1.  Examples of usable and abnormal monitoring data

    图 2  切片式幅值分布相似性度量示例

    Figure 2.  Similarity measurement between different slicing signals

    图 3  轴箱轴承上安装的监测传感器

    Figure 3.  Monitoring sensors installed on an axle box bearing

    图 4  轴箱轴承故障照片

    Figure 4.  Photographs of axle-box bearing faults

    图 5  轴箱轴承全寿命周期振动信号

    Figure 5.  Life-cycle vibration signals of axle-box bearings

    图 6  异常数据清洗效果

    Figure 6.  Illustration of abnormal data cleaning results

    图 7  轴箱轴承全寿命周期健康指标

    Figure 7.  Life-cycle health indicators of axle-box bearings

    图 8  不同采样时刻下的轴箱轴承未来退化轨迹拟合结果

    Figure 8.  Degradation trajectory fitting results of axle-box bearings under different sampling times

    图 9  轴箱轴承剩余寿命预测结果

    Figure 9.  Remaining useful life prediction results of axle-box bearings

    表  1  提出方法与现有方法的预测性能对比

    Table  1.   Performance comparison between the proposed method and existing prediction methods

    预测
    方法
    CRACS
    #15902-007#15903-003#15902-007#15903-003
    方法 1[14] 0.5263 0.6531 2.7034 3.1343
    方法 2[11] 0.3354 −0.1859 2.8750 4.0607
    方法 3[9] 0.6261 0.5731 2.5139 3.3002
    提出方法 0.7818 0.7488 2.3817 2.8369
    下载: 导出CSV

    表  2  3种不同预测方法的CRA值对比

    Table  2.   CRA value comparison of three prediction methods

    轴承编号失效位置文献[9]文献[16]提出方法
    Ber 1_1外圈0.90100.91860.9272
    Ber 1_2外圈0.88640.89920.9133
    Ber 1_3外圈0.88200.86630.9006
    Ber 1_4保持架0.73400.79760.8187
    Ber 1_5内、外圈0.85670.82930.8670
    下载: 导出CSV
  • [1] 中国城市轨道交通协会. 城市轨道交通2021年度统计和分析报告[R]. 北京: 中国城市轨道交通协会, 2022.
    [2] 裴洪,胡昌华,司小胜,等. 基于机器学习的设备剩余寿命预测方法综述[J]. 机械工程学报,2019,55(8): 1-13. doi: 10.3901/JME.2019.08.001

    PEI Hong, HU Changhua, SI Xiaosheng, et al. Review of machine learning based remaining useful life prediction methods for equipment[J]. Journal of Mechanical Engineering, 2019, 55(8): 1-13. doi: 10.3901/JME.2019.08.001
    [3] LEI Y G, LI N P, GONTARZ S, et al. A model-based method for remaining useful life prediction of machinery[J]. IEEE Transactions on Reliability, 2016, 65(3): 1314-1326. doi: 10.1109/TR.2016.2570568
    [4] EL-TAWIL K, JAOUDE A A. Stochastic and nonlinear-based prognostic model[J]. Systems Science & Control Engineering, 2013, 1(1): 66-81.
    [5] PAROISSIN C. Inference for the Wiener process with random initiation time[J]. IEEE Transactions on Reliability, 2016, 65(1): 147-157. doi: 10.1109/TR.2015.2456056
    [6] KHELIF R, CHEBEL-MORELLO B, MALINOWSKI S, et al. Direct remaining useful life estimation based on support vector regression[J]. IEEE Transactions on Industrial Electronics, 2017, 64(3): 2276-2285. doi: 10.1109/TIE.2016.2623260
    [7] WANG B, LEI Y G, LI N P, et al. Multiscale convolutional attention network for predicting remaining useful life of machinery[J]. IEEE Transactions on Industrial Electronics, 2021, 68(8): 7496-7504. doi: 10.1109/TIE.2020.3003649
    [8] HUANG C G, HUANG H Z, LI Y F. A bidirectional LSTM prognostics method under multiple operational conditions[J]. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8792-8802. doi: 10.1109/TIE.2019.2891463
    [9] WANG B, LEI Y G, LI N P, et al. A hybrid prognostics approach for estimating remaining useful life of rolling element bearings[J]. IEEE Transactions on Reliability, 2020, 69(1): 401-412. doi: 10.1109/TR.2018.2882682
    [10] 刘德昆,李强,王曦,等. 动车组轴箱轴承基于实测载荷的寿命预测方法[J]. 机械工程学报,2016,52(22): 45-54. doi: 10.3901/JME.2016.22.045

    LIU Dekun, LI Qiang, WANG Xi, et al. Life prediction method for EMU axle box bearings based on actual measured loadings[J]. Journal of Mechanical Engineering, 2016, 52(22): 45-54. doi: 10.3901/JME.2016.22.045
    [11] 赵珂,顾佳,姜喜民. 动车组转向架轴箱剩余寿命预测方法研究[J]. 软件,2020,41(3): 219-224. doi: 10.3969/j.issn.1003-6970.2020.03.052

    ZHAO Ke, GU Jia, JIANG Ximin. Research on prediction method of residual life of bogie axle box for multiple unit train[J]. Computer Engineering & Software, 2020, 41(3): 219-224. doi: 10.3969/j.issn.1003-6970.2020.03.052
    [12] 吕晟. 城市轨道交通车辆走行部轴箱轴承健康评估及寿命预测系统[J]. 城市轨道交通研究,2021,24(增1): 149-153. doi: 10.16037/j.1007-869x.2021.S1.033

    LYU Sheng. Health assessment and life prediction system for axle box bearing of urban rail transit vehicle running gear[J]. Urban Mass Transit, 2021, 24(S1): 149-153. doi: 10.16037/j.1007-869x.2021.S1.033
    [13] 刘嘉蔚,李奇,陈维荣,等. 基于核超限学习机和局部加权回归散点平滑法的PEMFC剩余使用寿命预测方法[J]. 中国电机工程学报,2019,39(24): 7272-7279,7500. doi: 10.13334/J.0258-8013.PCSEE.181614

    LIU Jiawei, LI Qi, CHEN Weirong, et al. Remaining useful life prediction method of PEMFC based on kernel extreme learning machine and locally weighted scatterplot smoothing[J]. Proceedings of the CSEE, 2019, 39(24): 7272-7279,7500. doi: 10.13334/J.0258-8013.PCSEE.181614
    [14] LI N P, LEI Y G, LIN J, et al. An improved exponential model for predicting remaining useful life of rolling element bearings[J]. IEEE Transactions on Industrial Electronics, 2015, 62(12): 7762-7773. doi: 10.1109/TIE.2015.2455055
    [15] SAXENA A, CELAYA J, SAHA B, et al. Metrics for offline evaluation of prognostic performance[J]. International Journal of Prognostics and Health Management, 2021, 1(1): 2153-2648.
    [16] DU W L, HOU X K, WANG H C. Time-varying degradation model for remaining useful life prediction of rolling bearings under variable rotational speed[J]. Applied Sciences, 2022, 12(8): 4044.1-4044.17. doi: 10.3390/app12084044
  • 加载中
图(9) / 表(2)
计量
  • 文章访问数:  378
  • HTML全文浏览量:  351
  • PDF下载量:  123
  • 被引次数: 0
出版历程
  • 收稿日期:  2022-03-31
  • 修回日期:  2022-07-03
  • 网络出版日期:  2022-12-17
  • 刊出日期:  2022-07-07

目录

    /

    返回文章
    返回