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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 MIKULOVICH Vladimir Ivanovich

康守强, 周月, 王玉静, 谢金宝, MIKULOVICH Vladimir Ivanovich. 基于改进SAE和双向LSTM的滚动轴承RUL预测方法. 自动化学报, 2022, 48(9): 2327−2336 doi: 10.16383/j.aas.c190796
引用本文: 康守强, 周月, 王玉静, 谢金宝, MIKULOVICH Vladimir Ivanovich. 基于改进SAE和双向LSTM的滚动轴承RUL预测方法. 自动化学报, 2022, 48(9): 2327−2336 doi: 10.16383/j.aas.c190796
Kang Shou-Qiang, Zhou Yue, Wang Yu-Jing, Xie Jin-Bao, Mikulovich Vladimir Ivanovich. RUL prediction method of a rolling bearing based on improved SAE and Bi-LSTM. Acta Automatica Sinica, 2022, 48(9): 2327−2336 doi: 10.16383/j.aas.c190796
Citation: Kang Shou-Qiang, Zhou Yue, Wang Yu-Jing, Xie Jin-Bao, Mikulovich Vladimir Ivanovich. RUL prediction method of a rolling bearing based on improved SAE and Bi-LSTM. Acta Automatica Sinica, 2022, 48(9): 2327−2336 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(LH-2019E058), 黑龙江省本科高校青年创新人才培养计划(UNPYSCT-2017091)和黑龙江省普通高校基本科研业务专项资金资助项目(LGYC2018JC022)资助
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. 本文通信作者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

    MIKULOVICH Vladimir Ivanovich: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和模式识别. 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), and 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 interest covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, and pattern recognition

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

    WANG Yu-Jing  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 interest covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, and pattern recognition. Corresponding author of this paper

    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 interest covers computer vision and natural language processing

    MIKULOVICH Vladimr Ivanovich Professor at Belarusian State University. He received his Ph.D. degree from Belarusian State University in 1975. His research interest covers non-stationary signal processing, fault diagnosis, state assessment and prediction technology, and 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  本文方法预测轴承1_7的当前p

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

    图  10  本文方法对轴承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 2002 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  本文预测方法与其他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
    本文方法 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预测误差结果对比(PHM2012轴承数据集) (%)

    Table  4  Comparison of RUL prediction results of different bearings (PHM2012 bearing datasets) (%)

    不同轴承本文方法方案 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 预测误差结果对比(XJTU-SY 轴承数据集) (%)

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

    不同轴承本文方法方案 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
  • [1] 刘建昌, 权贺, 于霞, 何侃, 李镇华. 基于参数优化VMD和样本熵的滚动轴承故障诊断. 自动化学报, 2022, 48(3): 808−819 doi: 10.16383/j.aas.190345

    Liu Jian-Chang, Quan He, Yu Xia, He Kan, Li Zhen-Hua. Rolling bearing fault diagnosis based on parameter optimization VMD and sample entropy. Acta Automatica Sinica, 2022, 48(3): 808−81 doi: 10.16383/j.aas.190345
    [2] 张正新, 胡昌华, 司小胜, 张伟. 双时间尺度下的设备随机退化建模与剩余寿命预测方法. 自动化学报, 2017, 43(10): 1789-1798

    Zhang Zheng-Xin, Hu Chang-Hua, Si Xiao-Sheng, Zhang Wei. Degradation modeling and remaining useful life prediction with bivariate time scale. Acta Automatica Sinica, 2017, 43(10): 1789-1798
    [3] 赵光权, 刘小勇, 姜泽东, 胡聪. 基于深度学习的轴承健康因子无监督构建方法. 仪器仪表学报, 2018, 39(6): 82-88

    Zhao Guang-Quan, Liu Xiao-Yong, Jiang Ze-Dong, Hu Cong. Unsupervised health indicator of bearing based on deep learning. Chinese Journal of Scientific Instrument, 2018, 39(6): 82-88
    [4] 杨宇, 张娜, 程军圣. 全参数动态学习深度信念网络在滚动轴承寿命预测中的应用. 振动与冲击, 2019, 38(10): 199-205+249

    Yang Yu, Zhang Na, Cheng Jun-Sheng. Global parameters dynamic learning deep belief networks and its application in rolling bearing life prediction. Journal of Vibration and Shock, 2019, 38(10): 199-205+249
    [5] Hinchi A Z, Tkiouat M. Rolling element bearing remaining useful life estimation based on a convolutional long-short-term memory network. Procedia Computer Science, 2018, 127: 123-132 doi: 10.1016/j.procs.2018.01.106
    [6] Li X, Zhang W, Ding Q. Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering and System Safety, 2019, 182: 208-218 doi: 10.1016/j.ress.2018.11.011
    [7] Cheriyadat A M. Unsupervised feature learning for aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(1): 439-451 doi: 10.1109/TGRS.2013.2241444
    [8] 孙文珺, 邵思羽, 严如强. 基于稀疏自动编码深度神经网络的感应电动机故障诊断. 机械工程学报, 2016, 52(9): 65-71 doi: 10.3901/JME.2016.09.065

    Sun Weng-Jun, Shao Si-Yu, Yan Ru-Qiang. Induction motor fault diagnosis based on deep neural network of sparse auto-encoder. Journal of Mechanical Engineering, 2016, 52(9): 65-71 doi: 10.3901/JME.2016.09.065
    [9] 陈宇, 温欣玲, 刘兆瑜, 马鹏阁. 稀疏自动编码器视觉特征融合的多弹分类算法研究. 红外与激光工程, 2018, 47(8): 386-393

    Chen Yu, Wen Xin-Ling, Liu Zhao-Yu, Ma Peng-Ge. Research of multi-missile classification algorithm based on sparse auto-encoder visual feature fusion. Infrared and Laser Engineering, 2018, 47(8): 386-393
    [10] 朱宵珣, 周沛, 苑一鸣, 徐博超, 韩中合. 基于KL-HVD的转子振动故障诊断方法研究. 振动与冲击, 2018, 37(16): 250-255

    Zhu Xiao-Xun, Zhou Pei, Yuan Yi-Ming, Xu Bo-Chao, Han Zhong-He. A study on the method of rotor vibration fault diagnosis based on KL-HVD. Journal of Vibration and Shock, 2018, 37(16): 250-255
    [11] Wang S X, Wang X, Wang S M, Wang D. Bi-directional long short-term memory method based on attention mechanism and rolling update for short-term load forecasting. Electrical Power and Energy Systems, 2019, 109: 470-479 doi: 10.1016/j.ijepes.2019.02.022
    [12] Bin Y, Yang Y, Shen F M, Xie N, Shen H T, Li X L. Describing video with attention-based bidirectional LSTM. IEEE Transactions on Cybernetics, 2019, 49(7): 2631-2641 doi: 10.1109/TCYB.2018.2831447
    [13] 刘国梁, 余建波. 知识堆叠降噪自编码器. 自动化学报, 2022, 48(3): 774−786 doi: 10.16383/j.aas.c190375

    Liu Guo-Liang, Yu Jian-Bo. Knowledge-based stacked denoising Autoencoder. Acta Automatica Sinica, 2022, 48(3): 774−786 doi: 10.16383/j.aas.c190375
    [14] Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014, 15: 1929-1958
    [15] Wu S, Gebraeel N, Lawley M A. A neural network integrated decision support system for condition-based optimal predictive maintenance policy. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 2007, 37(2): 226-236. doi: 10.1109/TSMCA.2006.886368
    [16] 史加荣, 王丹, 尚凡华, 张鹤于. 随机梯度下降算法研究进展. 自动化学报, 2020: https://doi.org/10.16383/j.aas.c190260 doi: 10.16383/j.aas.c190260

    Shi Jia-Rong, Wang Dan, Shang Fan-Hua, Zhang He-Yu. Research advances on stochastic gradient descent algorithms. Acta Automatica Sinica, 2020: https://doi.org/10.16383/j.aas.c190260 doi: 10.16383/j.aas.c190260
    [17] Nectoux P, Gouriveau R, Medjaher K, Ramasso E, Morello B, Zerhouni N, et al. PRONOSTIA: An experimental platform for bearings accelerated degradation tests. In: Proceedings of the Conference on Prognostics and Health Management. Denver, Colorado, USA: IEEE, 2012. 1−8
    [18] 石晓辉, 阳新华, 张向奎, 李文礼. 改进的形态差值滤波器在滚动轴承故障诊断中的应用. 重庆理工大学学报(自然科学), 2018, 32(1): 1-6

    Shi Xiao-Hui, Yang Xin-Hua, Zhang Xiang-Kui, Li Weng-Li. Application of improved morphological difference filter in fault diagnosis of rolling bearings. Journal of Chongqing University of Technology(Natural Science), 2018, 32(1): 1-6
    [19] Singleton R K, Strangas E G, Aviyente S. Extended kalman filtering for remaining-useful-life estimation of bearings. IEEE Transactions on Industrial Electronics, 2015, 62(3): 1781-1790 doi: 10.1109/TIE.2014.2336616
    [20] Guo L, Li N P, Jia F, Lei Y G, Lin J. A recurrent neural network based health indicator for remaining useful life prediction of bearings. Neurocomputing, 2017, 240: 98-109 doi: 10.1016/j.neucom.2017.02.045
    [21] Sheng H, Zheng Z, Enrico Z, Kan H. Condition assessment for the performance degradation of bearing based on a combinatorial feature extraction method. Digital Signal Processing, 2014, 27: 159-166 doi: 10.1016/j.dsp.2013.12.010
    [22] 雷亚国, 韩天宇, 王彪, 李乃鹏, 闫涛, 杨军. XJTU-SY滚动轴承加速寿命试验数据集解读. 机械工程学报, 2019, 55(16): 1-6 doi: 10.3901/JME.2019.16.001

    Lei Yang-Guo, Han Tian-Yu, Wang Biao, Li Nai-Peng, Yan Tao, Yang Jun. XJTU-SY rolling element bearing accelerated life test datasets: a tutorial. Journal of Mechanical Engineering, 2019, 55(16): 1-6 doi: 10.3901/JME.2019.16.001
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出版历程
  • 收稿日期:  2019-11-20
  • 录用日期:  2020-04-27
  • 网络出版日期:  2022-03-08
  • 刊出日期:  2022-09-16

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