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Denoising convolutional autoencoder configuration for condition monitoring of rotating machines

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Journal of the Brazilian Society of Mechanical Sciences and Engineering Aims and scope Submit manuscript

Abstract

In the present state of technology, rotating machine health monitoring is becoming economically mandatory. There is presently a clear trend to apply deep learning techniques to this area. This work analyzes some of the main supervised and unsupervised neural network current architectures and, as a consequence, proposes a novel denoising convolutional autoencoder model configuration applied to machine health monitoring. The configuration aims the main advantages of different conventional architectures for performance optimization over normalized time series signals. Experiments carried out based on vibration data extracted from bearings helped to validate the developed model and to determine the structure that offer the most satisfactory results. A comparison is made in terms of accuracy and robustness to noise between the proposed method and some related methods, including other published models. The objective is to assess the effectiveness of the proposed method through using two public experimental datasets produced under different machine operating conditions. The experimental results show that the proposed architecture provides greater classification accuracy and robustness in both case studies considering other published models.

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Abbreviations

AE:

Autoencoder

CAE:

Convolutional autoencoder

CNN:

Convolutional neural network

CWRU:

Case western reserve university

DAE:

Denoising autoencoder

DCAE:

Denoising convolutional autoencoder

FN:

False negative

FP:

False positive

MLP:

Multilayer perceptron

MSE:

Mean square error

ReLU:

Rectified linear unit

SGD:

Stochastic gradient descent

SNR:

Signal-to-noise ratio

TP:

True positive

References

  1. Liu R, Yang B, Zio E, Chen X (2018) Artificial intelligence for fault diagnosis of rotating machinery: a review. Mech Syst Signal Process 108:33–47

    Article  Google Scholar 

  2. Janssens O, Slavkovikj V, Vervisch B, Stockman K, Loccufier M, Verstockt S, Van de Walle R, Van Hoecke S (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345

    Article  Google Scholar 

  3. Sen A, Majumder MC, Mukhopadhyay S, Biswas RK (2017) Condition monitoring of rotating equipment considering the cause and effects of vibration : a brief review. Int J Mod Eng Res 7:36–49

    Google Scholar 

  4. Lei Y, Jia F, Lin J, Xing S, Ding SX (2016) An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Trans Ind Electron 63(5):3137–3147

    Article  Google Scholar 

  5. Sun J, Yan C, Wen J (2018) Intelligent bearing fault diagnosis method combining compressed data acquisition and deep learning. IEEE Trans Instrum Meas 67(1):185–195

    Article  Google Scholar 

  6. Chen X, Wang S, Qiao B, Chen Q (2018) Basic research on machinery fault diagnostics: past, present, and future trends. Front Mech Eng 13(2):264–291

    Article  Google Scholar 

  7. Zhang X, Chen W, Wang B, Chen X (2015) Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization. Neurocomputing 167(05):260–279

    Article  Google Scholar 

  8. Pan L, Xiao J, Hu S (2019) Intelligent fault diagnosis of rotating machinery using support vector machine and improved abc. In: Proceedings of the 2019 international conference on modeling, simulation and big data analysis (MSBDA 2019), Atlantis Press, pp 388–393

  9. Hu Q, Si XS, Zhang QH, Qin AS (2020) A rotating machinery fault diagnosis method based on multi-scale dimensionless indicators and random forests. Mech Syst Signal Process 139:106609

    Article  Google Scholar 

  10. Zhang R, Peng Z, Wu L, Yao B, Guan Y (2017) Fault diagnosis from raw sensor data using deep neural networks considering temporal coherence. Sensors 17(3):549

    Article  Google Scholar 

  11. Wu C, Jiang P, Ding C, Feng F, Chen T (2019) Intelligent fault diagnosis of rotating machinery based on one-dimensional convolutional neural network. Comput Ind 108:53–61

    Article  Google Scholar 

  12. Wei D, Wang K, Heyns S, Zuo MJ (2019) Convolutional neural networks for fault diagnosis using rotating speed normalized vibration. Advances in condition monitoring of machinery in non-stationary operations. Springer, Cham, pp 67–76

    Google Scholar 

  13. Chen Z, Gryllias K, Li W (2019) Mechanical fault diagnosis using convolutional neural networks and extreme learning machine. Mech Syst Signal Process 133:106272

    Article  Google Scholar 

  14. Xu G, Liu M, Jiang Z, Söffker D, Shen W (2019) Bearing fault diagnosis method based on deep convolutional neural network and random forest ensemble learning. Sensors 19(5):1088

    Article  Google Scholar 

  15. Xie Y, Zhang T (2017) Fault diagnosis for rotating machinery based on convolutional neural network and empirical mode decomposition. Shock Vib 3:1–12

    Google Scholar 

  16. Shao H, Jiang H, Zhao H, Wang F (2017) A novel deep autoencoder feature learning method for rotating machinery fault diagnosis. Mech Syst Signal Process 95:187–204

    Article  Google Scholar 

  17. Lu C, Wang ZY, Qin WL, Ma J (2017) Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification. Signal Process 130(7):377–388

    Article  Google Scholar 

  18. Liu G, Bao H, Han B (2018) A stacked autoencoder-based deep neural network for achieving gearbox fault diagnosis. Math Probl Eng 2018:1–10

    Google Scholar 

  19. Li C, Zhang W, Peng G, Liu S (2017) Bearing fault diagnosis using fully-connected winner-take-all autoencoder. IEEE Access 6:6103–6115

    Article  Google Scholar 

  20. Mathew V, Toby T, Singh V, Rao BM, Kumar MG (2017) Prediction of remaining useful lifetime (RUL) of turbofan engine using machine learning. In: 2017 IEEE International conference on circuits and systems (ICCS), IEEE, pp 306–311

  21. Aggarwal CC (2018) Neural networks and deep learning: a textbook. Springer International Publishing, Cham

    Book  MATH  Google Scholar 

  22. Serwa A (2017) Studying the effect of activation function on classification accuracy using deep artificial neural networks. J Remote Sens GIS 06(03):1–6

    Google Scholar 

  23. Verstraete D, Ferrada A, Droguett EL, Meruane V, Modarres M (2017) Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings. Shock Vib 2017:1–17

    Article  Google Scholar 

  24. Hoang DT, Kang HJ (2019) Rolling element bearing fault diagnosis using convolutional neural network and vibration image. Cognit Syst Res 53:42–50

    Article  Google Scholar 

  25. Vincent P, Larochelle H, Bengio Y, Manzagol PA (2008) Extracting and composing robust features with denoising autoencoders. In: Proceedings of the 25th international conference on machine learning, pp 1096–1103

  26. Jia F, Lei Y, Guo L, Lin J, Xing S (2018) A neural network constructed by deep learning technique and its application to intelligent fault diagnosis of machines. Neurocomputing 272:619–628

    Article  Google Scholar 

  27. Zhao R, Yan R, Chen Z, Mao K, Wang P, Gao RX (2019) Deep learning and its applications to machine health monitoring. Mech Syst Signal Process 115:213–237

    Article  Google Scholar 

  28. Verma NK, Gupta VK, Sharma M, Sevakula RK (2013) Intelligent condition based monitoring of rotating machines using sparse auto-encoders. In: 2013 IEEE Conference on prognostics and health management (PHM), pp 1–7

  29. Guo X, Liu X, Zhu E, Yin J (2017) Deep clustering with convolutional autoencoders. In: Liu D, Xie S, Li Y, Zhao D, El-Alfy ES (eds) Neural information processing. ICONIP 2017. Lecture notes in computer science, vol 10635. Springer, Cham, pp 373–382

  30. Shao H, Jiang H, Lin Y, Li X (2018) A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders. Mech Syst Signal Process 102:278–297

    Article  Google Scholar 

  31. Xia M, Li T, Shu T, Wan J, De Silva CW, Wang Z (2019) A two-stage approach for the remaining useful life prediction of bearings using deep neural networks. IEEE Trans Ind Inform 15(6):3703–3711

    Article  Google Scholar 

  32. Boyat AK, Joshi BK (2015) A review paper: noise models in digital image processing. Signal Image Process An Int J 6(2):63–75

    Article  Google Scholar 

  33. Mahamad AK, Saon S, Hiyama T (2010) Predicting remaining useful life of rotating machinery based artificial neural network. Comput Math Appl 60(4):1078–1087

    Article  MATH  Google Scholar 

  34. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  35. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on international conference on machine learning, p 448–456

  36. Roland U, Eseosa O (2014) Artificial intelligent techniques in real-time diagnosis of stator and rotor faults in induction machines. Int J Sci Eng Res 5(10):946–954

    Google Scholar 

  37. Du B, Xiong W, Wu J, Zhang L, Zhang L, Tao D (2017) Stacked convolutional denoising auto-encoders for feature representation. IEEE Trans Cybern 47(4):1017–1027

    Article  Google Scholar 

  38. Jiang G, He H, Yan J, Xie P (2019) Multiscale convolutional neural networks for fault diagnosis of wind turbine gearbox. IEEE Trans Ind Electron 66(4):3196–3207

    Article  Google Scholar 

  39. Daga AP, Fasana A, Marchesiello S, Garibaldi L (2019) The Politecnico di Torino rolling bearing test rig: description and analysis of open access data. Mech Syst Signal Process 120:252–273

    Article  Google Scholar 

  40. Ince T, Kiranyaz S, Eren L, Askar M, Gabbouj M (2016) Real-time motor fault detection by 1-d convolutional neural networks. IEEE Trans Ind Electron 63(11):7067–7075

    Article  Google Scholar 

  41. Case Western Reserve University Bearing Data Center Website (no date). https://csegroups.case.edu/bearingdatacenter/pages/download-data-file. Accessed 11 Dec 2019

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Acknowledgements

The authors thank the partial financial support of CAPES (Grant No. 88882.435258/2019-01) and CNPq, Brazil (Grant No. 312533/2017-9), and Politecnico di Torino and Case Western Reserve University for making the bearing datasets publicly available.

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Campinas, Mendeleyev Street, 200, Campinas, SP, 13083-860, Brazil

    Leonardo Franco de Godói & Eurípedes Guilherme de Oliveira Nóbrega

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  1. Leonardo Franco de Godói
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  2. Eurípedes Guilherme de Oliveira Nóbrega
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Correspondence to Leonardo Franco de Godói.

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Godói, L.F.d., Nóbrega, E.G.d.O. Denoising convolutional autoencoder configuration for condition monitoring of rotating machines. J Braz. Soc. Mech. Sci. Eng. 43, 53 (2021). https://doi.org/10.1007/s40430-020-02776-7

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  • DOI: https://doi.org/10.1007/s40430-020-02776-7

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