Skip to main content
SpringerLink
Log in

Detailed Comparison of Methods for Classifying Bearing Failures Using Noisy Measurements

  • Technical Article---Peer-Reviewed
  • Published:
Journal of Failure Analysis and Prevention Aims and scope Submit manuscript

Abstract

Rotary machines are key equipment in many industrial sectors, from mining operations to advanced manufacturing. Among the critical components of these machines are bearings, gearboxes, rotors, among others. These components tend to present failures, which can be catastrophic, with economic, safety and/or environmental consequences. Among the most established methods for classifying bearing faults, the envelope method has been widely used, with relative success, for several years. However, this method and its variations are difficult to automate and require extensive experience on the part of the analyst. We report that, while traditional methods (e.g., envelope) successfully classified bearing failures less than 45% of the time, machine learning methods were successful more than 62% of the time, and in some cases reaching 67%. This work differs from others in the sense that it uses all the available measurements from a well-known database, not just a subset. In addition, the measurements are taken at the motor base, which are more difficult to classify, and avoid using different segments of the same signal in both training and validation, thus reducing the possibility of overfitting. As a consequence, the results obtained are apparently inferior to those reported elsewhere, but probably closer to what one might expect in practical applications.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. W. Yang, P.J. Tavner, C.J. Crabtree, C.J. Crabtree, Y. Feng, Y. Qiu, Wind turbine condition monitoring: technical and commercial challenges. Wind Energy. 17, 673–693 (2014)

    Google Scholar 

  2. R.B. Randall, J. Antoni, Rolling element bearing diagnostics—A tutorial. Mech. Syst. Signal Process. 25, 485–520 (2011)

    Google Scholar 

  3. Report of Large Motor Reliability Survey of Industrial and Commercial Installations, Part I. IEEE Transactions on Industry Applications. IA-21, 853–864(1985)

  4. K. Fischer, F. Besnard, L. Bertling, Reliability-centered maintenance for wind turbines based on statistical analysis and practical experience. IEEE Trans. Energy Convers. 27, 184–195 (2012)

    Google Scholar 

  5. T. Slack, F. Sadeghi, Explicit finite element modeling of subsurface initiated spalling in rolling contacts. Tribol. Int. 43, 1693–1702 (2010)

    Google Scholar 

  6. D. Wang, K.L. Tsui, Q. Miao, Prognostics and health management: a review of vibration based bearing and gear health indicators. IEEE Access. 6, 665–676 (2018)

    CAS  Google Scholar 

  7. M. Cerrada, R.V. Sánchez, C. Li, F. Pacheco, D.R. Cabrera, J.V. De Olivera, R.E. Vasquez, A review on data-driven fault severity assessment in rolling bearings. Mech. Syst. Signal Process. 99, 169–196 (2018)

    Google Scholar 

  8. N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. Ser. A: Math. Phys. Eng. Sci. 454, 903–995 (1998)

    Google Scholar 

  9. Z. Peng, F. Chu, Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mech. Syst. Signal Process. 18, 199–221 (2004)

    Google Scholar 

  10. G. Cheng, X.-H. Chen, X.-L. Shan, H.-G. Liu, C.-F. Zhou, A new method of gear fault diagnosis in strong noise based on multi-sensor information fusion. J. Vib. Control 22, 1504–1515 (2014)

    Google Scholar 

  11. S. Bogoevska, M. Spiridonakos, E. Chatzi, E. Dumova-Jovanoska, R. Hoffer, A data-driven diagnostic framework for wind turbine structures: a holistic approach. Sensors 17(4), 720 (2017)

    Google Scholar 

  12. T. Han, D. Jiang, Q. Zhao, L. Wang, K. Yin, Comparison of random forest, artificial neural networks and support vector machine for intelligent diagnosis of rotating machinery. Transactions of the Institute of Measurement and Control. 40, 2681–2693 (2017)

    Google Scholar 

  13. D. Goyal, B.S. Vanraj, S.S.Dhami Pabla, Condition monitoring parameters for fault diagnosis of fixed axis gearbox: a review. Arch. Comput. Methods Eng. 24, 543–556 (2016)

    Google Scholar 

  14. S. Kang, D. Ma, Y. Wang, C. Lan, Q. Chen, V.I. Mikulovich, Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding. Mech. Syst. Signal Process. 86, 40–57 (2017)

    Google Scholar 

  15. J. Zarei, M.A. Tajeddini, H.R. Karimi, Vibration analysis for bearing fault detection and classification using an intelligent filter. Mechatronics 24, 151–157 (2014)

    Google Scholar 

  16. J. Ben Ali, N. Fnaiech, L. Saidi, B. Chebel-Morello, F. Fnaiech, Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals. Appl. Acoust. 89, 16–27 (2015)

    Google Scholar 

  17. D. Jiang, C. Liu, Machine condition classification using deterioration feature extraction and anomaly determination. IEEE Trans. Reliab. 60, 41–48 (2011)

    Google Scholar 

  18. Y. Lei, Z. He, Y. Zi, EEMD method and WNN for fault diagnosis of locomotive roller bearings. Expert Syst. Appl. 38, 7334–7341 (2011)

    Google Scholar 

  19. Y. Tian, J. Ma, C. Lu, Z. Wang, Rolling bearing fault diagnosis under variable conditions using LMD-SVD and extreme learning machine. Mech. Mach. Theory 90, 175–186 (2015)

    Google Scholar 

  20. D. Dou, S. Zhou, Comparison of four direct classification methods for intelligent fault diagnosis of rotating machinery. Appl. Soft Comput. 46, 459–468 (2016)

    Google Scholar 

  21. B. Zhou, Y. Cheng, Fault diagnosis for rolling bearing under variable conditions based on image recognition. Shock Vib. 2016, 1–14 (2016)

    CAS  Google Scholar 

  22. J. Zheng, H. Pan, J. Cheng, Rolling bearing fault detection and diagnosis based on composite multiscale fuzzy entropy and ensemble support vector machines. Mech. Syst. Signal Process. 85, 746–759 (2017)

    Google Scholar 

  23. C. Sobie, C. Freitas, M. Nicolai, Simulation-driven machine learning: bearing fault classification. Mech. Syst. Signal Process. 99, 403–419 (2018)

    Google Scholar 

  24. M. Singh, A.G. Shaik, Faulty bearing detection, classification and location in a three-phase induction motor based on Stockwell transform and support vector machine. Measurement 131, 524–533 (2019)

    Google Scholar 

  25. S. Kavathekar, N. Upadhyay, P. Kankar, Fault classification of ball bearing by rotation forest technique. Proc. Technol. 23, 187–192 (2016)

    Google Scholar 

  26. B. Paya, I. Esat, M. Badi, Artificial neural network based fault diagnostics of rotating machinery using wavelet transforms as a preprocessor. Mech. Syst. Signal Process. 11, 751–765 (1997)

    Google Scholar 

  27. B. Li, M.-Y. Chow, Y. Tipsuwan, J.C. Hung, Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans. Industr. Electron. 47, 1060–1069 (2000)

    Google Scholar 

  28. B. Samanta, K. Al-Balushi, Artificial neural network based fault diagnostics of rolling element bearings using time-domain features. Mech. Syst. Signal Process. 17, 317–328 (2003)

    Google Scholar 

  29. S. Abbasion, A. Rafsanjani, A. Farshidianfar, N. Irani, Rolling element bearings multi-fault classification based on the wavelet denoising and support vector machine. Mech. Syst. Signal Process. 21, 2933–2945 (2007)

    Google Scholar 

  30. V. Sugumaran, V. Muralidharan, K. Ramachandran, Feature selection using decision tree and classification through proximal support vector machine for fault diagnostics of roller bearing. Mech. Syst. Signal Process. 21, 930–942 (2007)

    Google Scholar 

  31. P. Konar, P. Chattopadhyay, Bearing fault detection of induction motor using wavelet and support vector machines (SVMs). Appl. Soft Comput. 11, 4203–4211 (2011)

    Google Scholar 

  32. C. Cortes, V. Vapnik, Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    Google Scholar 

  33. L. Breiman, Random forests. Mach. Learn. 45, 5–32 (2001)

    Google Scholar 

  34. B.-S. Yang, X. Di, T. Han, Random forests classifier for machine fault diagnosis. J. Mech. Sci. Technol. 22, 1716–1725 (2008)

    Google Scholar 

  35. T. Han, D. Jiang, Rolling bearing fault diagnostic method based on VMD-AR model and random forest classifier. Shock Vib. 2016, 1–11 (2016)

    Google Scholar 

  36. Z. Wang, Q. Zhang, J. Xiong, M. Xiao, G. Sun, J. He, Fault diagnosis of a rolling bearing using wavelet packet denoising and random forests. IEEE Sens. J. 17, 5581–5588 (2017)

    Google Scholar 

  37. T. Chen, C. Guestrin, XGBoost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD ‘16 2016. Epub ahead of print 2016. https://doi.org/10.1145/2939672.2939785

  38. D. Nielsen, Tree Boosting With XGBoost. M.Sc. Thesis, Norwegian University of Science and Technology, Norway (2016)

  39. D. Zhang, L. Qian, B. Mao, C. Huang, B. Huang, Y. Si, A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access. 6, 21020–21031 (2018)

    Google Scholar 

  40. R. Zhang, B. Li, B. Jiao, Application of XGboost algorithm in bearing fault diagnosis. IOP Conf. Ser.: Mater. Sci. Eng. 490, 072062 (2019)

    Google Scholar 

  41. Y. Lecun, Deep learning & convolutional networks. 2015 IEEE Hot Chips 27 Symposium (HCS) (2015). Epub ahead of print 2015. https://doi.org/10.1109/hotchips.2015.7477328

  42. M. Gan, C. Wang, C. Zhu, Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings. Mech. Syst. Signal Process. 72–73, 92–104 (2016)

    Google Scholar 

  43. F. Jia, Y. Lei, J. Lin, X. Zhou, N. Lu, Deep neural networks: a promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data. Mech. Syst. Signal Process. 72–73, 303–315 (2016)

    Google Scholar 

  44. R.B. Randall, Vibration-based condition monitoring: industrial, aerospace and automotive applications (Publication, A John Wiley and Sons Ltd, 2011)

    Google Scholar 

  45. A. Bravi, A. Longtin, A.J. Seely, Review and classification of variability analysis techniques with clinical applications. BioMed. Eng. OnLine 10, 90 (2011)

    Google Scholar 

  46. Bearing Data Center http://csegroups.case.edu/bearingdatacenter/home

  47. W.A. Smith, R.B. Randall, Rolling element bearing diagnostics using the Case Western Reserve University data: a benchmark study. Mech. Syst. Signal Process. 64–65, 100–131 (2015)

    Google Scholar 

Download references

Acknowledgments

J. Vargas-Machuca and A.M. Coronado are grateful to VRI-UNI and UNIFIM-UNI for the generous financial support. F.A. García is grateful to the Amistad Peruano-Ecuatoriana Scholarship from PRONABEC-Perú.

Author information

Authors and Affiliations

  1. Laboratorio de Sistemas Inteligentes, Facultad de Ingeniería Mecánica, Universidad Nacional de Ingeniería, Lima, Peru

    Juan Vargas-Machuca, Félix García & Alberto M. Coronado

Authors
  1. Juan Vargas-Machuca
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Félix García
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alberto M. Coronado
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alberto M. Coronado.

Ethics declarations

Conflict of interest

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Vargas-Machuca, J., García, F. & Coronado, A.M. Detailed Comparison of Methods for Classifying Bearing Failures Using Noisy Measurements. J Fail. Anal. and Preven. 20, 744–754 (2020). https://doi.org/10.1007/s11668-020-00872-3

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11668-020-00872-3

Keywords

Search

Navigation