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大数据背景下基于特征学习的机械设备剩余寿命预测

郭亮 李长根 高宏力 董勋 向守兵

郭亮, 李长根, 高宏力, 董勋, 向守兵. 大数据背景下基于特征学习的机械设备剩余寿命预测[J]. 西南交通大学学报, 2021, 56(4): 730-735, 768. doi: 10.3969/j.issn.0258-2724.20190528
引用本文: 郭亮, 李长根, 高宏力, 董勋, 向守兵. 大数据背景下基于特征学习的机械设备剩余寿命预测[J]. 西南交通大学学报, 2021, 56(4): 730-735, 768. doi: 10.3969/j.issn.0258-2724.20190528
GUO Liang, LI Changgen, GAO Hongli, DONG Xun, XIANG Shoubing. Residual Life Prediction of Mechanical Equipment Based on Feature Learning in Big Data Background[J]. Journal of Southwest Jiaotong University, 2021, 56(4): 730-735, 768. doi: 10.3969/j.issn.0258-2724.20190528
Citation: GUO Liang, LI Changgen, GAO Hongli, DONG Xun, XIANG Shoubing. Residual Life Prediction of Mechanical Equipment Based on Feature Learning in Big Data Background[J]. Journal of Southwest Jiaotong University, 2021, 56(4): 730-735, 768. doi: 10.3969/j.issn.0258-2724.20190528

大数据背景下基于特征学习的机械设备剩余寿命预测

doi: 10.3969/j.issn.0258-2724.20190528
基金项目: 国家自然科学基金(51905452);中央高校基本科研专项资金(2682019CX35,2018GF02)
详细信息
    作者简介:

    郭亮(1988—),男,副教授,博士,研究方向为机械设备故障诊断与寿命预测,E-mail:guoliang@swjtu.edu.cn

  • 中图分类号: TH878;TG115.28

Residual Life Prediction of Mechanical Equipment Based on Feature Learning in Big Data Background

  • 摘要: 传统数据驱动剩余寿命的预测方法是通过信号处理从监测数据中手动提取特征并构建健康指标,而在大数据背景下,手动提取特征需要特定专家知识并耗费大量人力,为解决该问题,提出了一种基于特征学习的机械设备剩余寿命预测方法——自适应特征学习寿命预测方法(AFLRULP). 该方法构建移动窗口数据矩阵解决单次采样中的数据波动问题,并建立了多层一维卷积神经网络将数据矩阵映射为机械设备的健康状态;根据失效阈值可以计算出机械设备的剩余寿命;采样轴承全寿命周期数据集合对提出的AFLRULP进行验证,并且与传统基于手动提取特征的方法进行寿命预测准确性的对比. 研究结果表明:AFLRULP不需要人工提取特征,可从原始监测数据映射为机械设备的性能状态与剩余寿命,相对于现有的基于手动提取特征的寿命预测方法,提出的方法在轴承寿命预测累积相对准确率上平均提高了0.20.

     

  • 图 1  自适应特征学习寿命预测方法

    Figure 1.  Adaptive feature learning based remaining useful life prediction

    图 2  剩余寿命预测与概率分布

    Figure 2.  Remaining life prediction and probability distribution

    图 3  不同方法所预测的剩余寿命

    Figure 3.  Residual life of bearing 1 and bearing 2 predicted by different methods

    图 4  不同寿命预测方法的相对准确率

    Figure 4.  Relative accuracy of different life prediction methods

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出版历程
  • 收稿日期:  2019-06-18
  • 修回日期:  2020-04-09
  • 网络出版日期:  2021-03-11
  • 刊出日期:  2021-08-15

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