Volume 35 Issue 6
Jun.  2020
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YU Guangbin, ZHUO Shi, YU Jun. Remaining useful life prediction of rolling bearings using, InfoLSGAN and AC algorithm[J]. Journal of Aerospace Power, 2020, 35(6): 1212-1221. doi: 10.13224/j.cnki.jasp.2020.06.011
Citation: YU Guangbin, ZHUO Shi, YU Jun. Remaining useful life prediction of rolling bearings using, InfoLSGAN and AC algorithm[J]. Journal of Aerospace Power, 2020, 35(6): 1212-1221. doi: 10.13224/j.cnki.jasp.2020.06.011
shu

Remaining useful life prediction of rolling bearings using, InfoLSGAN and AC algorithm

doi: 10.13224/j.cnki.jasp.2020.06.011
  • 1.

    Key Laboratory of Advanced Manufacturing and Intelligent Technology,Ministry of Education,Harbin University of Science and Technology,Harbin 150080,China

  • 2.

    School of Mechanical and Power Engineering,Harbin University of Science and Technology,Harbin 150080,China

  • 3.

    School of Automation,Harbin University of Science and Technology,Harbin 150080,China

  • Received Date: 2019-12-23
  • Publish Date: 2020-06-28
    Abstract
  • In order to solve the problem of low remaining useful life (RUL) accuracy of rolling bearings under small samples and noise interference,a RUL prediction method of rolling bearings using information least squares generative adversarial network (InfoLSGAN) and actor-critic (AC) algorithm was proposed. Stacked denoising autoencoder,information generative adversarial network and least squares generative adversarial network were integrated to construct InfoLSGAN,which can automatically extract interpretable and robust features from noisy data,and solve the problem of vanishing gradients. The training algorithm based on AC was utilized to train the InfoLSGAN to reduce the training time and accelerate the convergence. According to the InfoLSGAN after training,a softmax classifier was used to predict the rolling bearing RUL in test samples. The effectiveness of the proposed method was validated through an accelerated fatigue life experiment of rolling bearings. The experimental results demonstrated that when the signal-to-noise ratio was equal to 0,the proposed method increased the RUL prediction accuracy of rolling bearing test samples by at least 10%. In the case of small samples,the average accuracy of the RUL prediction of rolling bearings was 9584%.

     

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    • References(22)
  • [1]
    ZHOU Linghao,DUAN Fang,CORSAR M,et al.A study on helicopter main gearbox planetary bearing fault diagnosis［J］.Applied Acoustics,2019,147:4-14.
    [2]
    KUNDU P,DARPE A K,KULKARNL M S.Weibull accelerated failure time regression model for remaining useful life prediction of bearing working under multiple operating conditions［J］.Mechanical Systems and Signal Processing,2019,134:106302.1-106302.19.
    [3]
    田晶,李有儒,艾延廷.一种基于Deep-GBM的航空发动机中介轴承故障诊断方法［J］.航空动力学报,2019,34(4):756-763. TIAN Jing,LI Youru,AI Yanting.Fault diagnosis of aero-engine inter-shaft based on Deep-GBM［J］.Journal of Aerospace Power,2019,34(4):756-763.(in Chinese)
    [4]
    LEI Yaguo,LI Naipeng,GUO Liang,et al.Machinery health prognostics:a systematic review from data acquisition to RUL prediction［J］.Mechanical Systems and Signal Processing,2018,104:799-834.
    [5]
    张杰毅,陈果,谢阶栋,等.球轴承接触疲劳寿命预估的损伤力学-有限元法［J］.航空动力学报,2019,34(10):2246-2255. ZHANG Jieyi,CHEN Guo,XIE Jiedong,et al.Damage mechanics-finite element method for contact fatigue life prediction of ball bearing［J］.Journal of Aerospace Power,2019,34(10):2246-2255.(in Chinese)
    [6]
    AHMAD W,KHAN S A,KIM J M.A hybrid prognostics technique for rolling element bearings using adaptive predictive models［J］.IEEE Transactions on Industrial Electronics,2018,65(2):1577-1584.
    [7]
    LIU Xiongjun,SONG Ping,YANG Cheng,et al.Prognostics and health management of bearings based on logarithmic linear recursive least-squares and recursive maximum likelihood estimation［J］.IEEE Transactions on Industrial Electronics,2018,65(2):1549-1558.
    [8]
    HU Yaogang,LI Hui,SHI Pingping,et al.A prediction method for the real-time remaining useful life of wind turbine bearings based on the Wiener process［J］.Renewable Energy,2018,127:452-460.
    [9]
    BOUMAHDI M,RECHAK S,HANINI S.Analysis and prediction of defect size and remaining useful life of thrust ball bearings:modelling and experiment procedures［J］.Arabian Journal for Science and Engineering,2017,42(11):4535-4546.
    [10]
    ELFORJANI M,SHANBR S.Prognosis of bearing acoustic emission signals using supervised machine learning［J］.IEEE Transactions on Industrial Electronics,2018,65(7):5864-5871.
    [11]
    AHMAD W,KHAN S A,KIM J M.Enhanced particle filtering for bearing remaining useful life prediction of wind turbine drivetrain gearboxes［J］.IEEE Transactions on Industrial Electronics,2019,66(6):4738-4748.
    [12]
    SAIDI L,ALI J B,BECHHOFER E,et al.Wind turbine high-speed shaft bearings health prognosis through a spectral Kurtosis-derived indices and SVR［J］.Applied Acoustics,2017,120:1-8.
    [13]
    AYE S A,HEYNS P S.An integrated Gaussian process regression for prediction of remaining useful life of slow speed bearings based on acoustic emission［J］.Mechanical Systems and Signal Processing,2017,84:485-498.
    [14]
    ZHU Jun,CHEN Nan,PENG Weiwen.Estimation of bearing remaining useful life based on multiscale convolutional neural network［J］.IEEE Transactions on Industrial Electronics,2019,66(4):3208-3216.
    [15]
    REN Lei,SUN Yaqiang,CUI Jin,et al.Bearing remaining useful life prediction based on deep autoencoder and deep neural networks［J］.Journal of Manufacturing Systems,2018,48:71-77.
    [16]
    XIA Min,LI Teng,SHU Tongxin,et al.A two-stage approach for the remaining useful life prediction of bearings using deep neural networks［J］.IEEE Transactions Informatics,2019,15(6):3703-3711.
    [17]
    LI Xingqiu,JIANG Hongkai,XIONG Xiong,et al.Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network［J］.Mechanism and Machine Theory,2019,133:229-249.
    [18]
    VINCENT P,LAROCHELLE H,LAJOIE I,et al.Stacked denoising autoencoders:learning useful representations in a deep network with a local denoising criterion［J］.Journal of Machine Learning Research,2010,11:3371-3408.
    [19]
    MAO Xudong,LI Qing,XIE Haoran,et al.Least squares generative adversarial networks［C］∥Proceedings of the 16th IEEE International Conference on Computer Vision.Venice,Italy:IEEE Computer Society,2017:2813-2821.
    [20]
    KONDA V R,TSITSIKLIS J N,Actor-critic algorithms［C］∥Proceedings of the 13th Annual Neural Information Processing Systems.Denver,USA:Neural Information Processing Systems Foundation,2000:1008-1014.
    [21]
    CHEN Xi,DUAN Yan,HOUTHOOFT R,et al.InfoGAN:interpretable representation learning by information maximizing generative adversarial nets［C］∥Proceedings of the 30th Annual Conference on Neural Information Processing Systems.Barcelona,Spain:Neural Information Processing Systems Foundation,2016:2172-2180.
    [22]
    田晶,王英杰,刘丽丽,等.基于Birge-Massart阈值降噪与EEMD谱峭度的滚动轴承故障特征提取［J］.航空动力学报,2019,34(6):1339-1408.
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