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基于改进SAE和双向LSTM的滚动轴承RUL预测方法

康守强 周月 王玉静 谢金宝 MIKULOVICHV.I.

康守强, 周月, 王玉静, 谢金宝, MIKULOVICHV.I.. 基于改进SAE和双向LSTM的滚动轴承RUL预测方法. 自动化学报, 2020, 46(x): 1−10 doi: 10.16383/j.aas.c190796
引用本文: 康守强, 周月, 王玉静, 谢金宝, MIKULOVICHV.I.. 基于改进SAE和双向LSTM的滚动轴承RUL预测方法. 自动化学报, 2020, 46(x): 1−10 doi: 10.16383/j.aas.c190796
Kang Shou-Qiang, Zhou Yue, Wang Yu-Jing, Xie Jin-Bao, Mikulovich. RUL prediction method of a rolling bearing based on improved sae and bi-lstm. Acta Automatica Sinica, 2020, 46(x): 1−10 doi: 10.16383/j.aas.c190796
Citation: Kang Shou-Qiang, Zhou Yue, Wang Yu-Jing, Xie Jin-Bao, Mikulovich. RUL prediction method of a rolling bearing based on improved sae and bi-lstm. Acta Automatica Sinica, 2020, 46(x): 1−10 doi: 10.16383/j.aas.c190796

基于改进SAE和双向LSTM的滚动轴承RUL预测方法

doi: 10.16383/j.aas.c190796
基金项目: 国家自然科学基金(51805120), 黑龙江省自然科学基金(LH2019E058), 黑龙江省本科高校青年创新人才培养计划(UNPYSCT-2017091), 黑龙江省普通高校基本科研业务专项资金资助项目(LGYC2018JC022)
详细信息
    作者简介:

    康守强:哈尔滨理工大学电气与电子工程学院教授. 2011年获白俄罗斯国立大学博士学位. 主要研究方向为非平稳信号处理, 故障诊断,状态评估与预测技术和模式识别. E-mail: kangshouqiang@163.com

    周月:哈尔滨理工大学电气与电子工程学院硕士研究生. 主要研究方向为振动信号处理. E-mail: zhouyue_student@163.com

    王玉静:哈尔滨理工大学电气与电子工程学院副教授. 2015年获哈尔滨工业大学博士学位. 主要研究方向为非平稳信号处理, 故障诊断,状态评估与预测技术和模式识别. E-mail: mirrorwyj@163.com

    谢金宝:哈尔滨理工大学电气与电子工程学院副教授. 2012年获白俄罗斯国立大学博士学位. 主要研究方向为计算机视觉和自然语言处理. E-mail: jbxpost@163.com

    MIKULOVICHV.I.:白俄罗斯国立大学教授. 1975年获白俄罗斯国立大学博士学位. 主要研究方向为非平稳信号处理, 故障诊断、状态评估与预测技术, 模式识别. E-mail: falcon@tut.by

RUL Prediction Method of a Rolling Bearing Based on Improved SAE and Bi-LSTM

Funds: Supported by National Natural Science Foundation of China (51805120), Natural Science Foundation of Heilongjiang Province (LH2019E058), University Nursing Program for YoungScholars with Creative Talents in Heilongjiang Province (UNPYSCT-2017091), Fundamental Research Foundation for Universities of Heilongjiang Province(LGYC2018JC022)
More Information
    Author Bio:

    KANG Shou-Qiang Professor at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. He received his Ph. D. degree from Belarusian State University in 2011. His research interests covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, pattern recognition

    ZHOU Yue Master student at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. Her research interest covers vibration signal processing

    WANG Yu-Jing Associate professor at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. She received her Ph. D. degree from Harbin Institute of Technology in 2015. Her research interests covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, pattern recognition

    XIE Jin-Bao Associate professor at the College of Electrical and Electronic Engineering, Harbin University of Science and Technology. He received his Ph. D. degree from Belarusian State University in 2012. His research interests covers computer vision and natural language processing

    MIKULOVICH.I. Professor of Belarusian State University. He received his Ph. D. degree from Belarusian State University in 1975. His research interests covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, pattern recognition

  • 摘要: 针对稀疏自动编码器(Sparse auto encoder, SAE)采用sigmoid激活函数容易造成梯度消失的问题, 用一种新的Tan函数替代原有的sigmoid函数; 针对SAE采用Kullback-Leibler(KL) 散度进行稀疏性约束在回归预测方面的局限性, 以dropout机制替代KL散度实现网络的稀疏性. 利用改进SAE对滚动轴承振动信号进行无监督深层特征自适应提取, 无需人工设计标签进行有监督微调. 同时, 考虑到滚动轴承剩余使用寿命(Remaining useful life, RUL)预测方法一般仅考虑过去信息而忽略未来信息, 引入双向长短时记忆网络(Bi-directional long short-term memory, Bi-LSTM)构建滚动轴承RUL的预测模型. 在2个轴承数据集上的实验结果均表明, 所提基于改进SAE和Bi-LSTM的滚动轴承RUL预测方法不仅可以提高模型的收敛速度而且具有较低的预测误差.
  • 图  1  AE结构

    Fig.  1  The structure of AE

    图  2  sigmoid函数及其导函数曲线

    Fig.  2  The curves of sigmoid function and its derivative

    图  3  Tan函数及其导函数曲线

    Fig.  3  The curves of Tan function and its derivative

    图  4  LSTM单元内部结构

    Fig.  4  Internal structure of the LSTM cell

    图  5  Bi-LSTM网络展开图

    Fig.  5  Unfolded Bi-LSTM network

    图  6  滚动轴承RUL预测流程

    Fig.  6  Flow chart of RUL prediction for rolling bearings

    图  7  轴承1_1时域振动信号及归一化后的频域幅值谱

    Fig.  7  The time domain vibration signal and normalized amplitude spectrum of the bearing1_1

    图  8  轴承1_1部分特征趋势曲线

    Fig.  8  The trend curve of partial features of the bearing1_1

    图  9  所提方法预测轴承1_7的当前p

    Fig.  9  The current p value of bearing 1_7 predicted by the proposed method

    图  10  所提方法对轴承1_7的RUL预测结果

    Fig.  10  RUL prediction result of bearing 1_7 by the proposed method

    图  11  特征提取所消耗时间的对比(PHM2012轴承数据集)

    Fig.  11  Comparison of the time consuming of feature extraction (PHM2012 bearing datasets)

    图  12  3种方案对轴承1_7的RUL预测结果

    Fig.  12  RUL prediction results of bearing 1_7 by three schemes

    图  13  特征提取所消耗时间的对比(XJTU-SY轴承数据集)

    Fig.  13  Comparison of the time consuming of feature extraction (XJTU-SY Bearing Datasets)

    表  1  实验数据(PHM2012轴承数据集)

    Table  1  Experimental data (PHM2012 bearing datasets)

    数据集划分不同轴承非全寿数据(组)全寿数据(组)
    训练集 1_1 - 2803
    1_2 - 871
    2_1 - 911
    2_2 - 797
    3_1 - 515
    3_2 - 1637
    测试集 1_3 1802 2375
    1_4 1139 1428
    1_5 2302 2463
    1_6 2302 2448
    1_7 1502 2259
    2_3 1202 1955
    2_4 612 751
    2_5 2 002 2311
    2_6 572 701
    2_7 172 230
    3_3 352 434
    下载: 导出CSV

    表  2  3种优化算法的训练误差

    Table  2  Training error of three optimization algorithms

    不同优化算法AdamRMSPropSGDM
    MSE0.00080.00110.0009
    MAE0.04500.07940.0485
    MAPE0.22920.33020.3394
    MSPE0.01830.02130.0406
    RMSE0.06240.09850.0651
    误差之和0.35570.53040.4945
    下载: 导出CSV

    表  3  所提预测方法与其他3种方案的构成

    Table  3  The composition of the proposed prediction method and other three schemes

    预测方法特征提取模型预测模型
    本文 改进SAE Bi-LSTM
    方案1 SAE Bi-LSTM
    方案2 改进SAE LSTM
    方案3 SAE LSTM
    下载: 导出CSV

    表  4  不同轴承RUL预测结果对比(PHM2012轴承数据集)

    Table  4  Comparison of RUL prediction results of different bearings (PHM2012 Bearing Datasets)

    不同轴承误差$ Er_i$(%)
    本文所提方法方案1方案2方案3文献[20]文献[21]
    1_3 8.03 0.52 0.70 −6.98 43.28 −31.76
    1_4 −8.30 0.70 −3.81 2.42 67.55 62.76
    1_5 −44.72 −26.09 −45.34 −110.56 −22.98 −136.03
    1_6 −2.74 −23.29 21.92 −13.70 21.23 −32.88
    1_7 −3.04 7.13 −9.51 −33.03 17.83 −11.09
    2_3 −4.12 −20.85 −15.94 −1.73 37.84 44.22
    2_4 0.72 −3.60 −0.72 −27.30 −19.42 −55.40
    2_5 −6.15 16.83 −38.51 12.62 54.37 68.61
    2_6 3.10 −37.21 −13.95 −6.20 −13.95 −51.94
    2_7 1.72 −1.72 5.17 −1.72 −55.17 −68.97
    3_3 −15.85 2.44 2.44 17.07 3.66 −21.96
    平均误差 −6.49 −7.74 −9.81 −15.37 32.48 53.24
    平均得分 0.576 0.522 0.477 0.425 0.263 0.065
    下载: 导出CSV

    表  5  实验数据(XJTU-SY轴承数据集)

    Table  5  Experimental data (XJTU-SY bearing datasets)

    数据集划分不同轴承非全寿数据(组)全寿数据(组)
    训练集 1_1 123
    1_2 161
    2_1 491
    2_2 161
    3_1 2538
    3_2 2496
    测试集 1_3 126 158
    1_4 98 122
    1_5 42 52
    2_3 426 533
    2_4 34 42
    2_5 271 339
    3_3 297 371
    3_4 1212 1515
    3_5 91 114
    下载: 导出CSV

    表  6  不同轴承RUL预测结果对比(XJTU-SY轴承数据集)

    Table  6  Comparison of RUL prediction results of different bearings (XJTU-SY bearing datasets)

    不同轴承误差$ Er_i$(%)
    本文所提方法方案1方案2方案3
    1_3 15.63 21.88 12.50 18.75
    1_4 8.33 −8.33 −4.17 20.83
    1_5 −10.00 −30.00 20.00 −10.00
    2_3 −24.30 −21.78 15.89 −23.36
    2_4 −12.50 −25.00 −25.00 −12.50
    2_5 10.29 27.94 14.71 22.06
    3_3 31.08 23.78 17.57 22.97
    3_4 −18.25 5.83 −50.83 −36.96
    3_5 4.35 −26.09 −8.70 −39.13
    平均误差 0.51 −3.53 −0.89 −4.15
    平均得分 0.419 0.282 0.418 0.267
    下载: 导出CSV
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  • 收稿日期:  2019-11-20
  • 录用日期:  2020-04-27
  • 网络出版日期:  2022-03-08

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