Volume 2 Issue 2
Mar.  2022
Turn off MathJax
Article Contents
Jijie HOU, Bo MA, Libing LIANG, Ming ZHANG. An early warning method for mechanical fault detection based on adversarial auto-encoders[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(2): 2022006. doi: 10.51393/j.jamst.2022006
Citation: Jijie HOU, Bo MA, Libing LIANG, Ming ZHANG. An early warning method for mechanical fault detection based on adversarial auto-encoders[J]. Journal of Advanced Manufacturing Science and Technology , 2022, 2(2): 2022006. doi: 10.51393/j.jamst.2022006

An early warning method for mechanical fault detection based on adversarial auto-encoders

doi: 10.51393/j.jamst.2022006
  • Received Date: 2022-01-02
  • Accepted Date: 2022-03-01
  • Rev Recd Date: 2022-02-10
  • Available Online: 2022-03-15
  • Publish Date: 2022-03-14
  • The vibration signal of mechanical equipment is non-linear and non-stationary, and it is difficult to fully reflect the operation state of equipment through traditional fixed threshold alarm method. While the early warning method based on multi-feature parameter fusion relies on manual experience to extract features, which is difficult to ensure the accuracy of the extracted features and cannot achieved good early warning effect. To solve this problem, a feature self-learning method based on adversarial auto-encoders is proposed in this paper, which encodes high-dimensional monitoring data in normal state into low-dimensional vectors with certain statistical laws and uses it as a benchmark to detect abnormalities in the operating state of the equipment in time by measuring the difference between the encoded features of real-time monitoring data and the benchmark. The actual application cases of reciprocating compressors show that the proposed method can detect the weak signs of equipment fault at the early stage, and realize early warning. At the same time, by comparing with the Auto-Encoders network-based warning method and the Dirichlet process mixture model-based warning method, It is verified that the method in this paper has more advantages in terms of warning accuracy and warning time.

  • loading
  • [1]
    . Chen XF, Wang SB, Cheng L. Research on matching synchronous compression transformation of aero-engine fastchanging signal. Journal of Mechanical Engineering 2019;55(13):13-22.
    . Zhou T, Hu MH, He Y, et al. Vibration features of rotor unbalance and rub-impact compound fault. Journal of Advanced Manufacturing Science and Technology 2022;2(1):2022002.
    . Ma B, Zheng F. Application of denoising autoencoder for the early warning of mechanical faults. Journal of Beijing University of Chemical Technology (Natural Science Edition) 2019;46(5):53-59[Chinese].
    . Li WH, Dai BX, Zhang SH. Bearing performance degradation assessment based on Wavelet packet entropy and Gaussian mixture model. Journal of Vibration and Shock 2013;32(21):35- 40[Chinese].
    . Ma B, Zhao Y, Zhang Y, et al. Machinery early fault detection based on Dirichlet process mixture model. IEEE Access 2019; 7: 89226-89233.
    . Shi HT, Guo J, Yuan Z, et al. Incipient fault detection of rolling element bearings based on deep EMD-PCA algorithm. Shock and Vibration 2020;2020:8871433.
    . Gu YT, Song L, Xu TJ, et al. Research on wind turbine gearbox fault warning method under variable working conditions. China Mechanical Engineering 2014;25(10):1346-1351 [Chinese].
    . Shao HD, Jiang HK, Li XQ, et al. Rolling bearing fault detection using continuous deep belief network with locally linear embedding. Computers in Industry 2018;96:27-39.
    . Liu SJ, Hu YW, Li C, et al. Machinery condition prediction based on wavelet and support vector machine. Journal of Intelligent Manufacturing 2017;28(4):1045-1055.
    . Singh M, Shaik AG. Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine. Measurement 2019;131:524-533.
    . Zhou JM, Guo HJ, Zhang L, et al. Bearing performance degradation assessment using lifting wavelet packet symbolic entropy and SVDD. Shock and Vibration 2016;2016: 3086454.
    . Balanica V, Liao L, Claussen H, et al. A multi-model approach for anomaly detection and diagnosis using vibration signals. 2013 IEEE Conference on Prognostics and Health Management (PHM); 2013 Jun; Gaithersburg, MD, USA; 2013. p. 1-7.
    . Lecun Y, Bengio Y, Hinton GE. Deep learning. Nature 2015;521(7553):436-444.
    . AlShorman O, Irfan M, Saad N, et al. A review of artificial intelligence methods for condition monitoring and fault diagnosis of rolling element bearings for induction motor. Shock and Vibration 2020;2020:8843759.
    . Kemker R, Kanan C. Self-taught feature learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing 2017;55(5):2693-2705.
    . Liu QX, Ma ZH, Zhu XN, et al. Performance assessment and anomaly detection of wind turbine based on long short time memory-auto encoder neural network. Computer Integrated Manufacturing Systems 2019;25(12):3209-3219 [Chinese].
    . Zhao H, Liu H, Liu H, et al. Condition monitoring and fault diagnosis of wind turbine generator based on stacked autoencoder network. Automation of Electric Power Systems 2018;42(11):102-108.
    . Wu X, Jiang GQ, Wang X, et al. A multi-level-denoising autoencoder approach for wind turbine fault detection. IEEE Access 2019;7:59376-59387.
    . Wang YR, Sun GD, Jin Q. Imbalanced sample fault diagnosis of rotating machinery using conditional variational auto-encoder generative adversarial network. Applied Soft Computing 2020; 92:106-333.
    . Liu ZH, Lu BL, Wei HL, et al. Deep adversarial domain adaptation model for bearing fault diagnosis. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2021; 51(7):4217- 4226.
    . Ji LY, Lv X, Tao FF, et al. Water conservancy data completion method based on adversarial autoencoders network. Computer Engineering 2019;45(4):307-310 [Chinese].
    . Makhzani A, Shlens J, Jaitly N, et al. Adversarial autoencoders. Computer Science 2015.
    . Wang JY, Zhou WG, Tang JH, et al. Unregularized autoencoder with generative adversarial networks for image generation. Proceedings of the 26th ACM International Conference on Multimedia. 2018.p. 22-26.
    . Bengio Y. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2009;2(1):1-27.
    . Liu H, Zhou JZ, Xu YH, et al. Unsupervised fault diagnosis of rolling bearings using a deep neural network based on generative adversarial networks. Neurocomputing 2018;315:412-424.
    . Christian L, Lucas T, Ferenc H, et al. Photo-realistic single image super-resolution using a generative adversarial network. IEEE Conference on Computer Vision and Pattern Recognition 2016; 2:105-114.
    . Ma JY, Yu W, Liang PW, et al. FusionGAN: A generative adversarial network for infrared and visible image fusion. Information Fusion 2019;48:11-26.
    . Yao DC, Sun Q, Yang JW, et al. Railway fastener fault diagnosis based on generative adversarial network and residual network model. Shock and Vibration 2020;2020:8823050.
    . Zhang W, Li X, Jia XD, et al. Machinery fault diagnosis with imbalanced data using deep generative adversarial networks. Measurement 2020;152:107377.
    . Shao SY, Wang P, Yan RQ. Generative adversarial networks for data augmentation in machine fault diagnosis. Computers in Industry 2019;106:85-93.
    . Zheng Z, Zheng L, Yang Y. Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. Proceedings of the IEEE International Conference on Computer Vision(ICCV) 2017;2017.p.3774-3782.
    . Lin J. Divergence measures based on the Shannon entropy. IEEE Transactions on Information Theory 1991;37(1):145-151.
    . Ma B, Zhao Y, Qi LC. Application of variational auto-encoder in mechanical fault early warning. Computer Engineering and Applications 2019;55(12):245-249 [Chinese].
  • 加载中


    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索


    Article Metrics

    Article views (1186) PDF downloads(67) Cited by()
    Proportional views


    DownLoad:  Full-Size Img  PowerPoint