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基于关系网络的轴承剩余使用寿命预测方法

赵志宏 张然 孙诗胜

赵志宏, 张然, 孙诗胜. 基于关系网络的轴承剩余使用寿命预测方法. 自动化学报, 2023, 49(7): 1549−1557 doi: 10.16383/j.aas.c211195
引用本文: 赵志宏, 张然, 孙诗胜. 基于关系网络的轴承剩余使用寿命预测方法. 自动化学报, 2023, 49(7): 1549−1557 doi: 10.16383/j.aas.c211195
Zhao Zhi-Hong, Zhang Ran, Sun Shi-Sheng. Bearing remaining useful life prediction based on relation network. Acta Automatica Sinica, 2023, 49(7): 1549−1557 doi: 10.16383/j.aas.c211195
Citation: Zhao Zhi-Hong, Zhang Ran, Sun Shi-Sheng. Bearing remaining useful life prediction based on relation network. Acta Automatica Sinica, 2023, 49(7): 1549−1557 doi: 10.16383/j.aas.c211195

基于关系网络的轴承剩余使用寿命预测方法

doi: 10.16383/j.aas.c211195
基金项目: 国家自然科学基金 (11972236, 11790282), 石家庄铁道大学研究生创新项目(YC2022056)资助
详细信息
    作者简介:

    赵志宏:石家庄铁道大学教授. 2012年获得北京交通大学博士学位. 主要研究方向为机械故障诊断, 机器学习, 信号处理和动力学分析.E-mail: hb_zhaozhihong@126.com

    张然:石家庄铁道大学信息科学与技术学院硕士研究生. 主要研究方向为故障诊断, 状态评估与预测, 大数据分析. 本文通信作者. E-mail: sjz_zhangran@126.com

    孙诗胜:石家庄铁道大学信息科学与技术学院硕士研究生. 主要研究方向为故障诊断, 状态评估与预测, 大数据分析. E-mail: lxr_sunshisheng@126.com

Bearing Remaining Useful Life Prediction Based on Relation Network

Funds: Supported by National Natural Science Foundation of China (11972236, 11790282) and Graduate Innovation Funding Project of Shijiazhuang Tiedao University (YC2022056)
More Information
    Author Bio:

    ZHAO Zhi-Hong Professor at Shijiazhuang Tiedao University. He received his Ph.D. degree from Beijing Jiaotong University in 2012. His research interest covers diagnosis of mechanical equipment, machine learning, signal processing, and dynamic analysis

    ZHANG Ran Master student at the School of Information Science and Technology, Shijiazhuang Tiedao University. Her research interest covers fault diagnosis, state assessment and prediction, and big data analysis. Corresponding author of this paper

    SUN Shi-Sheng Master student at the School of Information Science and Technology, Shijiazhuang Tiedao University. His research interest covers fault diagnosis, state assessment and prediction, and big data analysis

  • 摘要: 针对轴承全寿命周期数据获取困难、训练样本少的问题, 提出一种基于关系网络的轴承剩余使用寿命(Remaining useful life, RUL)预测方法. 关系网络是一种基于度量的元学习方法, 在少量训练样本下, 具有快速学习新任务的优点. 设计了一种基于关系网络的轴承健康评估模型, 利用关系网络的嵌入模块提取轴承状态特征, 利用关系模块度量轴承状态特征之间的相似性, 基于相似性构建轴承健康指标(Health indicator, HI); 对健康指标进行Savitzky-Golay滤波平滑处理, 降低振荡对预测结果的影响; 最后利用线性函数对健康指标进行拟合, 得到轴承RUL预测值. 为验证所提方法的有效性, 在PHM2012轴承实测数据集上进行实验. 结果表明, 所得健康指标能够反映轴承的退化趋势, 所得RUL预测结果与空间卷积长短期记忆神经网络 (Convolutional long short-term memory neural network, ConvLSTM)、Transformer、循环神经网络(Recurrent neural network, RNN)、卷积神经网络(Convolutional neural network, CNN) + 长短期记忆网络 (long short-term memory network, LSTM )、编码器−解码器(Encoder-decoder) + 注意力机制 (Attention mechanism)方法相比, 误差百分比分别减少了1.67%, 3.40%, 9.02%, 13.71%, 30.48%. 该方法在少量训练样本的基础上可以取得较好的预测结果, 具有一定的应用价值.
  • 图  1  关系网络结构

    Fig.  1  Relation network structure

    图  2  关系网络模型结构

    Fig.  2  Structure of relational network model

    图  3  基于关系网络的轴承RUL预测流程

    Fig.  3  Bearing RUL prediction process based on relation network

    图  4  PRONOSTIA实验台

    Fig.  4  PRONOSTIA test bench

    图  5  轴承时域振动信号

    Fig.  5  Bearing time domain vibration signal

    图  6  训练集轴承健康指标

    Fig.  6  Training set bearing health indicators

    图  7  轴承健康指标

    Fig.  7  Bearing health indicators

    图  8  轴承RUL预测结果

    Fig.  8  Bearing RUL prediction results

    表  1  PHM2012轴承数据集

    Table  1  PHM2012 bearing dataset

    工况 工况1 工况2 工况3
    训练集 轴承1_1 轴承2_1 轴承3_1
    轴承1_2 轴承2_2 轴承3_2
    轴承1_5 轴承2_4
    轴承1_6 轴承2_5
    轴承1_7 轴承2_7
    测试集 轴承1_3 轴承2_3轴承3_3
    轴承1_4轴承2_6
    下载: 导出CSV

    表  2  不同模型参数量对比

    Table  2  Comparison of different model parameters

    方法 参数量 (k)
    本文方法 78.61
    ConvLSTM220.50
    Transformer6461.44
    CNN+LSTM1136.64
    下载: 导出CSV

    表  3  轴承RUL预测结果

    Table  3  Bearing RUL prediction results

    轴承 当前时刻 (10 s)真实寿命 (10 s)预测寿命 (10 s)本文方法 (%)文献 [13] (%) 文献 [28] (%) 文献 [11] (%) 文献 [29] (%) 文献 [12] (%)
    轴承1_3 1801 573 937 −63.53 33.68 74.17 74.17 54.73 7.62
    轴承1_4 1138 290 338 −16.55 47.24 −0.69 −0.69 38.69 −157.71
    轴承2_3 1201 753 1005 −33.46 −32.80 61.36 61.36 75.53 81.24
    轴承2_6 571 129 131 −1.55 8.52 0.78 0.78 17.87 24.92
    轴承3_3 351 82 77 6.09 7.32 1.22 1.22 2.93 2.09
    平均误差 24.24 25.91 27.64 33.26 37.95 54.72
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-12-15
  • 录用日期:  2022-03-13
  • 网络出版日期:  2022-05-30
  • 刊出日期:  2023-07-20

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