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Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks

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Abstract

Determining the optimal features that are invariant under changes in the rotational speed variations of rolling element bearings is a challenging task. To address this issue, this paper proposes an acoustic emission (AE) analysis-based bearing fault diagnosis invariant under fluctuations of rotational speeds using envelope spectrums (ES) and a convolutional neural network (CNN). The ES extracted from the raw AE signals provides valuable information about the characteristic defect frequency peaks and variations to bearing rotational speeds when faults appear on a bearing. The proposed method employs CNN to automatically extract high quality features and classify bearing defects. In the experiment, a CNN trained on a dataset corresponding to one revolutions per minute (RPM) is used to detect patterns from datasets corresponding to other RPMs to verify that the classification is accurate and invariant under rotation speed fluctuations. The efficacy of the proposed method is verified on AE-based low-speed bearing data under various rotational speeds. The experimental results show that the proposed method is effective at detecting bearing failures, provides an average classification accuracy of about 86% under fluctuating RPM, and outperforms other state-of-the-art algorithms.

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References

  • Ahmed HOA, Wong MLD, Nandi AK (2016) Effects of deep neural network parameters on classification of bearing faults. In: IECON 2016—42nd annual conference of the IEEE Industrial Electronics Society, 23–26 Oct 2016. pp 6329–6334. https://doi.org/10.1109/IECON.2016.7793957

  • Appana DK, Islam MR, Kim J-M (2017) Reliable fault diagnosis of bearings using distance and density similarity on an enhanced k-NN. In: Australasian conference on artificial life and computational intelligence. Springer, pp 193–203

  • Chen Z, Li C, Sanchez R-V (2015) Gearbox fault identification and classification with convolutional neural networks. Shock Vib 2015:1–10. https://doi.org/10.1155/2015/390134

    Article  Google Scholar 

  • Chen Z, Zeng X, Li W, Liao G (2016) Machine fault classification using deep belief network. In: 2016 IEEE international instrumentation and measurement technology conference proceedings, 23–26 May 2016. pp 1–6. https://doi.org/10.1109/I2MTC.2016.7520473

  • Christian Gerber MC (2017) Number plate detection with a multi-convolutional neural network approach with optical character recognition for mobile devices. J Inf Process Syst 12:100–108. https://doi.org/10.3745/JIPS.04.0022

    Article  Google Scholar 

  • Felten D (2003) Understanding bearing vibration frequencies. Electrical Apparatus Service Association (EASA), St Louis

    Google Scholar 

  • Flandrin P, Rilling G, Goncalves P (2004) Empirical mode decomposition as a filter bank. IEEE Signal Process Lett 11:112–114

    Article  Google Scholar 

  • Guo X, Chen L, Shen C (2016) Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis. Measurement 93:490–502

    Article  Google Scholar 

  • 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 63:7067–7075

    Article  Google Scholar 

  • Janssens O et al (2016) Convolutional neural network based fault detection for rotating machinery. J Sound Vib 377:331–345

    Article  Google Scholar 

  • Kang M, Kim J, Kim JM, Tan ACC, Kim EY, Choi BK (2015) Reliable fault diagnosis for low-speed bearings using individually trained support vector machines with kernel discriminative feature analysis. IEEE Trans Power Electron 30:2786–2797. https://doi.org/10.1109/TPEL.2014.2358494

    Article  Google Scholar 

  • Kang M, Islam MR, Kim J, Kim J-M, Pecht M (2016) A hybrid feature selection scheme for reducing diagnostic performance deterioration caused by outliers in data-driven diagnostics. IEEE Trans Ind Electron 63:3299–3310

    Article  Google Scholar 

  • Karpathy A, Toderici G, Shetty S, Leung T, Sukthankar R, Fei-Fei L (2014) Large-scale video classification with convolutional neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1725–1732

  • Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  • Lee D, Siu V, Cruz R, Yetman C (2016) Convolutional neural net and bearing fault analysis. In: Proceedings of the international conference on data mining (DMIN). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p 194

  • Li R, He D, Zhu J (2012) Investigation on full ceramic bearing fault diagnostics using vibration and AE sensors. In: 2012 IEEE conference on prognostics and health management (PHM). IEEE, pp 1–12

  • Ming Y, Chen J, Dong G (2011) Weak fault feature extraction of rolling bearing based on cyclic Wiener filter and envelope spectrum. Mech Syst Signal Process 25:1773–1785

    Article  Google Scholar 

  • Patil AB, Gaikwad JA, Kulkarni JV (2016) Bearing fault diagnosis using discrete Wavelet Transform and Artificial Neural Network. In: 2016 IEEE 2nd international conference on applied and theoretical computing and communication technology (iCATccT), IEEE, pp 399–405

  • Randall RB, Antoni J (2011) Rolling element bearing diagnostics—a tutorial. Mech Syst Signal Process 25:485–520

    Article  Google Scholar 

  • Rauber TW, de Assis Boldt F, Varejão FM (2015) Heterogeneous feature models and feature selection applied to bearing fault diagnosis. IEEE Trans Ind Electron 62:637–646

    Article  Google Scholar 

  • Ruchika Malhotra RJ (2017) Prediction & assessment of change prone classes using statistical & machine learning techniques. J Inf Process Syst 13:778–804. https://doi.org/10.3745/JIPS.04.0013

    Article  Google Scholar 

  • Sanjeev Kumar MC (2017) Detection of microcalcification using the wavelet based adaptive sigmoid function and neural network. J Inf Process Syst 13:703–715

    Google Scholar 

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

    MathSciNet  MATH  Google Scholar 

  • Sullivan GP, Pugh R, Melendez AP, Hung WD (2004) O&M best practices—a guide achieving operational efficiency, Release 2.0, Pacific Northwest National Laboratory

  • Sun S, Guo Y, Gao Y (2015) Experimental investigation on double impulses phenomenon of outer race spalled rolling element bearings based on acoustic emission signals. In: 2015 IEEE international conference on information and automation, IEEE, pp 1931–1935

  • Uddin J, Kang M, Nguyen DV, Kim J-M (2014) Reliable fault classification of induction motors using texture feature extraction and a multiclass support vector machine. Math Probl Eng 2014:814593. https://doi.org/10.1155/2014/814593

    Article  Google Scholar 

  • Wang J, Gongxing W, Sun Y, Wan L, Jiang D (2009) Fault diagnosis of Underwater Robots based on recurrent neural network. In: 2009 IEEE international conference on robotics and biomimetics (ROBIO), 19–23 Dec 2009. pp 2497–2502. https://doi.org/10.1109/ROBIO.2009.5420479

  • Yang Y, Yu D, Cheng J (2007) A fault diagnosis approach for roller bearing based on IMF envelope spectrum and SVM. Measurement 40:943–950

    Article  Google Scholar 

  • Yu N, Yu Z, Gu F, Li T, Tian X, Pan Y (2017) Deep learning in genomic and medical image data analysis: challenges and approaches. J Inf Process Syst 13:204–214

    Google Scholar 

  • Zhang W, Peng G, Li C (2017) Bearings fault diagnosis based on convolutional neural networks with 2-d representation of vibration signals as input. In: MATEC Web of conferences, EDP sciences, p 13001

    Article  Google Scholar 

  • Zheng L, Yuan H, Wang X, Yin H (2016) Fault diagnosis of transformer based on principal component analysis and self-organizing map neural network. In: 2016 IEEE international conference on high voltage engineering and application (ICHVE), IEEE, pp 1–4

  • Zhou Y, Chen J, Dong G, Xiao W, Wang Z (2012) Application of the horizontal slice of cyclic bispectrum in rolling element bearings diagnosis. Mech Syst Signal Process 26:229–243

    Article  Google Scholar 

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Acknowledgements

This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (Nos. 20162220100050, 20161120100350, and 20172510102130). It was also funded in part by The Leading Human Resource Training Program of the Regional Neo industry through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, and Future Planning (NRF-2016H1D5A1910564), in part by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2016R1D1A3B03931927), and in part by the development of basic fusion technology in the electric power industry (Ministry of Trade, Industry & Energy, 201301010170D).

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  1. Electrical and Computer Engineering, University of Ulsan, Ulsan, South Korea

    Dileep K. Appana, Alexander Prosvirin & Jong-Myon Kim

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  1. Dileep K. Appana
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  2. Alexander Prosvirin
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  3. Jong-Myon Kim
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Correspondence to Jong-Myon Kim.

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Communicated by G. Yi.

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Appana, D.K., Prosvirin, A. & Kim, JM. Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks. Soft Comput 22, 6719–6729 (2018). https://doi.org/10.1007/s00500-018-3256-0

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