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Learning via acceleration spectrograms of a DC motor system with application to condition monitoring

  • Wo Jae LeeEmail author
  • Haiyue Wu
  • Aihua Huang
  • John W. Sutherland
ORIGINAL ARTICLE
  • 51 Downloads

Abstract

In a highly automated manufacturing plant, the reliability of manufacturing equipment is critical for normal operation. A sudden machine breakdown can bring unexpected downtime, shorter lifespan of equipment, and lower operational efficiency. Breakdowns can lead to defective parts and consume extra energy—issues that are undesirable from an environmental standpoint—and also erode productivity and increase costs. To improve machine tool reliability, a machine may be continuously monitored to track its health condition. Monitoring a machine often provides large amounts of data that must be processed to distill useful information. Electric motors are found in many pieces of common manufacturing equipment. Deep learning methods can be combined with data collected on motors, e.g., acceleration time-frequency data, to identify motor condition. In this paper, three state-of-the-art deep learning architectures are evaluated for their ability to effectively monitor motor condition. Experiments are performed on a lab-scale motor test bed to secure condition data for several common motor faults. Tri-axial acceleration data are collected and converted into 2D images (spectrograms) using the power spectral density function. Some of these experiments are used to tune the deep learning algorithms, and others are used to test the proposed monitoring methods. The relative performances of the architectures are assessed, and it is demonstrated that the use of time-frequency images within a deep learning context can efficiently handle large amounts of data and effectively monitor the motor condition.

Keywords

Deep learning Motor condition monitoring Convolutional neural network Motor test bed 

Notes

Funding information

This work is supported by the Wabash Heartland Innovation Network (WHIN).

Compliance with Ethical Standards

Disclaimer

Any opinions, findings, conclusions, and/or recommendations expressed are those of the authors and do not necessarily reflect the view of the WHIN.

References

  1. 1.
    Tao F, Qi Q, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf Syst 48:157–169.  https://doi.org/10.1016/j.jmsy.2018.01.006 CrossRefGoogle Scholar
  2. 2.
    DeVor RE, Chang T, Sutherland JW (2006) Statistical quality design and control, 2nd edn. Prentice Hall, New YorkGoogle Scholar
  3. 3.
    Zhang Y, Ren S, Liu Y, Si S (2017) A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products. J Clean Prod 142:626–641CrossRefGoogle Scholar
  4. 4.
    Peng Y, Dong M, Zuo MJ (2010) Current status of machine prognostics in condition-based maintenance: a review. Int J Adv Manuf Technol 50:297–313CrossRefGoogle Scholar
  5. 5.
    Lee WJ, Wu H, Yun H, et al. (2019) Predictive maintenance of machine tool systems using artificial intelligence techniques applied to machine condition data. Procedia CIRP 80:506–511.  https://doi.org/10.1016/j.procir.2018.12.019 CrossRefGoogle Scholar
  6. 6.
    Ren L, Cui J, Sun Y, Cheng X (2017) Multi-bearing remaining useful life collaborative prediction: a deep learning approach. J Manuf Syst 43:248–256CrossRefGoogle Scholar
  7. 7.
    Huo Z, Zhang Y, Francq P, et al. (2017) Incipient fault diagnosis of roller bearing using optimized wavelet transform based multi-speed vibration signatures. IEEE Access 5:19442–19456.  https://doi.org/10.1109/ACCESS.2017.2661967 CrossRefGoogle Scholar
  8. 8.
    Verstraete D, Ferrada A, Droguett EL, et al. (2017) Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock Vib 2015:1–17Google Scholar
  9. 9.
    Zhao R, Yan R, Chen Z, et al. (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237.  https://doi.org/10.1016/j.ymssp.2018.05.050 CrossRefGoogle Scholar
  10. 10.
    Zhu X, Bain M (2017) B-CNN: branch convolutional neural network for hierarchical classification. CoRR abs/1709.0Google Scholar
  11. 11.
    LeCun Y, Bottou L, Bengio Y, Haffiner P (1998) Gradient-based learning applied to document recognition. In: Proceedings of the IEEE, pp 2278–2324CrossRefGoogle Scholar
  12. 12.
    Li LJ, Su H, Lim Y, Fei-Fei L (2012) Objects as attributes for scene classificationGoogle Scholar
  13. 13.
    Szegedy C, Liu W, Jia Y, et al. (2015) Going deeper with convolutions Christian. 2015 IEEE Conf Comput Vis Pattern Recognit 9:1–9.  https://doi.org/10.1002/jctb.4820 CrossRefGoogle Scholar
  14. 14.
    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. 2016 IEEE Conf Comput Vis Pattern Recognit, pp 770–778Google Scholar
  15. 15.
  16. 16.
    Yu J (2012) Health condition monitoring of machines based on hidden markov model and contribution analysis. IEEE Trans Instrum Meas 61:2200–2211.  https://doi.org/10.1109/TIM.2012.2184015 CrossRefGoogle Scholar
  17. 17.
    Wu H, Yu Z, Wang Y (2017) Real-time FDM machine condition monitoring and diagnosis based on acoustic emission and hidden semi-Markov model. Int J Adv Manuf Technol 90:2027–2036.  https://doi.org/10.1007/s00170-016-9548-6 CrossRefGoogle Scholar
  18. 18.
    Pezzani CM, Fontana JM, Donolo PD et al (2018) SVM-based system for broken rotor bar detection in induction motors. In: 2018 IEEE ANDESCON. IEEE, pp 1–6Google Scholar
  19. 19.
    Bhat NN, Dutta S, Vashisth T, et al. (2016) Tool condition monitoring by SVM classification of machined surface images in turning. Int J Adv Manuf Technol 83:1487–1502.  https://doi.org/10.1007/s00170-015-7441-3 CrossRefGoogle Scholar
  20. 20.
    Kane PV, Andhare AB (2016) Application of psychoacoustics for gear fault diagnosis using artificial neural network. J Low Freq Noise. Vib Act Control 35:207–220.  https://doi.org/10.1177/0263092316660915 CrossRefGoogle Scholar
  21. 21.
    Le Cun Y, Boser B, Denker JS et al (1990) Handwritten digit recognition with a back-propagation network. In: Advances in Neural Information Processing Systems 2. Morgan Kaufmann, pp 396–404Google Scholar
  22. 22.
    Jing L, Zhao M, Li P, Xu X (2017) A convolutional neural network based feature learning and fault diagnosis method for the condition monitoring of gearbox. Measurement 111:1–10.  https://doi.org/10.1016/j.measurement.2017.07.017 CrossRefGoogle Scholar
  23. 23.
    Chen Z, Li C, Sanchez R (2015) Gearbox fault identification and classification with convolutional neural networks. Shock Vib 2015:1–10Google Scholar
  24. 24.
    Ince T, Kiranyaz S, Member S, Eren L (2016) Real-time motor fault detection by 1-D convolutional neural networks. IEEE Trans Ind Electron 63:7067–7075.  https://doi.org/10.1109/TIE.2016.2582729 CrossRefGoogle Scholar
  25. 25.
    Eren L, Turker I, Kiranyaz S (2019) A generic intelligent bearing fault diagnosis system using compact adaptive 1D CNN classifier. J Signal Process Syst 91:179–189CrossRefGoogle Scholar
  26. 26.
    Mahmood F, Toots M, Öfverstedt L-G, Skoglund U (2018) Algorithm and architecture optimization for 2D discrete Fourier transforms with simultaneous edge artifact removal. In: International Journal of Reconfigurable Computing, pp 1–17CrossRefGoogle Scholar
  27. 27.
    Hoang D, Kang H (2019) Sciencedirect Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cogn Syst Res 53:42–50.  https://doi.org/10.1016/j.cogsys.2018.03.002 CrossRefGoogle Scholar
  28. 28.
    Sharma N, Jain V, Mishra A (2018) Analysis of convolutional neural networks for document image classification. In: Procedia Computer Science. Elsevier B.V., pp 377–384Google Scholar
  29. 29.
    Scherer D, Andreas M, Behnke S (2010) Evaluation of pooling operations in convolutional architectures for object recognition. In: Artificial Neural Networks - ICANN 2010 - 20th International Conference, pp 92–101CrossRefGoogle Scholar
  30. 30.
    Bouvrie J (2006) Notes on convolutional neural networks. http://cogprints.org/5869/1/cnn_tutorial.pdf. Accessed 1 May 2019
  31. 31.
    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks alex. Proc IEEE 86:2278–2323.  https://doi.org/10.1109/5.726791 CrossRefGoogle Scholar
  32. 32.
    Girdhar P, Scheffer C (2004) Practical machinery vibration analysis and predictive maintenanceGoogle Scholar
  33. 33.
    Rajendra BR, Bhaskar S (2013) Condition monitoring of gear box by using motor. Int J Sci Res Publ 3:1–6Google Scholar
  34. 34.
    U.S. DOE (2012) Energy tips: MOTOR SYSTEMSGoogle Scholar
  35. 35.
    Jung J, Member S, Bin Lee S, et al. (2016) Induction motors with sleeve bearings. IEEE Trans Energy Convers 31:1377–1386.  https://doi.org/10.1109/TEC.2016.2583473 CrossRefGoogle Scholar
  36. 36.
    Ganchev P, Malehorn D, Bigbee WL, Gopalakrishnan V (2011) Transfer learning of classification rules for biomarker discovery and verification from molecular profiling studies. J Biomed Inform 44:1–22.  https://doi.org/10.1016/j.jbi.2011.04.009 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Wo Jae Lee
    • 1
    Email author
  • Haiyue Wu
    • 1
  • Aihua Huang
    • 1
  • John W. Sutherland
    • 1
  1. 1.Laboratory for Sustainable Manufacturing, Environmental and Ecological EngineeringPurdue UniversityWest LafayetteUSA

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