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滚动轴承剩余使用寿命预测综述

张金豹 邹天刚 王敏 桂鹏 戈红霞 王成

张金豹, 邹天刚, 王敏, 桂鹏, 戈红霞, 王成. 滚动轴承剩余使用寿命预测综述[J]. 机械科学与技术, 2023, 42(1): 1-23. doi: 10.13433/j.cnki.1003-8728.20200489
引用本文: 张金豹, 邹天刚, 王敏, 桂鹏, 戈红霞, 王成. 滚动轴承剩余使用寿命预测综述[J]. 机械科学与技术, 2023, 42(1): 1-23. doi: 10.13433/j.cnki.1003-8728.20200489
ZHANG Jinbao, ZOU Tiangang, WANG Min, GUI Peng, GE Hongxia, WANG Cheng. Review on Remaining Useful Life Prediction of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 1-23. doi: 10.13433/j.cnki.1003-8728.20200489
Citation: ZHANG Jinbao, ZOU Tiangang, WANG Min, GUI Peng, GE Hongxia, WANG Cheng. Review on Remaining Useful Life Prediction of Rolling Bearing[J]. Mechanical Science and Technology for Aerospace Engineering, 2023, 42(1): 1-23. doi: 10.13433/j.cnki.1003-8728.20200489

滚动轴承剩余使用寿命预测综述

doi: 10.13433/j.cnki.1003-8728.20200489
详细信息
    作者简介:

    张金豹(1986-), 副研究员, 博士, 研究方向为机械关键零部件的故障诊断与寿命预测, zjb1357@163.com

    通讯作者:

    邹天刚, 研究员, 硕士, tigazou@163.com

  • 中图分类号: TH133.33

Review on Remaining Useful Life Prediction of Rolling Bearing

  • 摘要: 滚动轴承作为旋转机械的关键零部件,其剩余使用寿命(RUL)预测对生产维修和人身安全具有重要意义。由于滚动轴承复杂多变的工作环境,使得同工况的参考样本少而变工况的参考样本较多,具有不平衡、不完整、无标签及噪声干扰等特性,增加了滚动轴承RUL预测的困难。随着大数据时代的来临和人工智能的发展,滚动轴承RUL预测方法也变得更加丰富。因此,在故障预测与健康管理(PHM)的框架下,对滚动轴承失效模式和故障数据特点进行阐述,对故障特征提取、降维和融合方法以及得到的性能退化指标分别进行了分类和对比分析。结合数据驱动算法,对滚动轴承RUL的预测方法、模型选择和评估标准进行了梳理和对比。最后对滚动轴承RUL预测未来的发展趋势进行了展望。
  • 图  1  实验轴承损伤

    图  2  从系统到数据采集

    图  3  滚动轴承测试装置

    图  4  降维方法概况

    图  5  MS中心#2-1测试轴承外圈故障全寿命周期振动信号

    图  6  轴承磨损程度的动态影响

    图  7  预测方法选择

    图  8  滚动轴承退化过程拟合示意图

    表  1  时域统计特征

    下载: 导出CSV

    表  2  频域特征

    下载: 导出CSV

    表  3  剩余使用寿命定义

    定义 参考文献
    根据当前机器状态和过去的操作配置文件, 预测在发生故障之前还剩下多少时间 [117]
    显示结构、系统或组件是否能够在其整个生命周期内合理地保证执行其功能, 如果不能, 则估计剩余的使用寿命 [118]
    从当前时间点到可使用寿命的结束点之间的时间长度 [119]
    估计失效前的时间, 或剩余的使用寿命, 以及相关的置信值 [120]
    对一种或多种现有和未来失效模式的失效和风险的时间估计 [121]
    当前时间到首次穿越阈值的时间长度 [122]
    下载: 导出CSV

    表  4  剩余使用寿命预测方法分类

    预测算法分类 参考文献
    统计方法人工智能方法基于模型的方法 [117]
    基于失效数据基于应力基于效果 [123]
    基于物理模型数据驱动方法组合模型 [124]
    基于物理模型基于知识的方法数据驱动方法组合模型 [125]
    基于力学基于概率统计基于信息新技术 [126]
    基于数理统计理论基于数据驱动基于模型基于相似性 [127]
    基于经验模型基于物理模型基于数据驱动模型混合预测方法 [128]
    基于物理模型的方法基于统计模型的方法人工智能方法 [129]
    基于模型方法基于可靠度方法和概率模型数据驱动方法数据驱动和可靠度模型结合 [19]
    基于模型方法数据驱动方法组合模型 [130]
    基于可靠度方法基于物理预测方法数据驱动预测方法混合预测方法 [13]
    基于物理模型的方法基于统计模型的方法人工智能方法混合方法 [88]
    下载: 导出CSV

    表  5  预测精度评估指标

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