Skip to main content

Computational intelligence methods for rolling bearing fault detection

Abstract

Rolling bearings are very commonly used in many industrial applications. Therefore, detecting problems in their performance is very essential. This can be done by analyzing vibration signals resulting from their operation, as recorded by accelerometers. The current investigation aims to evaluate the efficiency of various computational intelligence algorithms, in detecting and correctly classifying faults in rolling bearings. A supplementary goal is to determine the optimum location for the accelerometers, in order for the aforementioned algorithms to identify faults on each bearing. Results indicate the most efficient computational intelligence methods for fulfilling the aforementioned goals, and suggest an optimum experimental setup, in order to successfully detect bearing faults.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

References

  1. Abbasion S, Rafsanjani A, Farshidianfar A, Irani N (2007) Mech Syst Signal Process 21:2933–2945

    Article  Google Scholar 

  2. Thomas P (1998) Fault detection and diagnosis in engineering systems: In: Gertler JJ (ed) vol 10(9). Marcel Dekker Inc., New York, ISBN 0-8247-9427-3, September 2002, pp 1037–1038

  3. Purushotham V, Narayanan S, Prasad SAN (2005) Multi-fault diagnosis of rolling bearing elements using wavelet analysis and hidden Markov model based fault recognition, NDT&E International, pp 1–11

  4. Bently D (1989) Bently Nevada Co., Predictive maintenance through the monitoring and diagnostics of rolling element bearings, Applications Note ANO44, pp 2–8. http://www.ge-mcs.com/pt/online-learning-center/predictive-maintenance-through-the-monitoring-and-diagnostics-of-rolling-element-bearings.html

  5. Wang WJ, McFadden PD (1996) Application of wavelets to gearbox vibration signals for fault detection. J Sound Vib 192:927–939

    Article  Google Scholar 

  6. Shiroishi J, Li Y, Liang S, Kurfess T, Danyluk S (1997) Bearing condition diagnostics via vibration and acoustic emission measurements. Mech Syst Signal Process 11:693–705

    Article  Google Scholar 

  7. Scholkopf B (1998) SVMs—a practical consequence of learning theory. IEEE Intell Syst 13:18–19

    Google Scholar 

  8. Dellomo MR (1999) Helicopter gearbox fault detection: a neural network-based approach. Trans ASME J Vib Acoust 121:265–272

    Article  Google Scholar 

  9. Li B, Chow MY, Tipsuwan Y, Hung JC (2000) Neural-network-based motor rolling bearing fault diagnosis. IEEE Trans Ind Electron 47(5):1010–1060

    Article  Google Scholar 

  10. Jack LB, Nandi AK (2001) Support vector machines for detection and characterization of rolling element bearing faults. Proc Inst Mech Eng Part C J Mech Eng Sci 215:1065–1074

    Article  Google Scholar 

  11. Nikolaou NG, Antoniadis IA (2002) Rolling element bearing fault diagnosis using wavelet packets. NDT&E Int 35:197–205

    Article  Google Scholar 

  12. Samanta B, Al-Balushi KR, Al-Araimi SA (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Eng Appl Artif Intell 16:657–665

    Article  Google Scholar 

  13. Al-Ghamd AM, Mba D (2006) A comparative experimental study on the use of acoustic emission and vibration analysis for bearing defect identification and estimation of defect size. Mech Syst Signal Process 20(7):1537–1571

    Article  Google Scholar 

  14. Trendafilova I (2010) Mech Syst Signal Process 24:1858–1869

    Article  Google Scholar 

  15. de Moura EP, Souto CR, Silva AA, Irmao MAS (2011) Mech Syst Signal Process 25:1765–1772

    Article  Google Scholar 

  16. Zhu X, Zhang Y, Zhu Y (2012) Intelligent fault diagnosis of rolling bearing based on kernel neighborhood rough sets and statistical features. J Mech Sci Technol 26(9):2649–2657

    Article  Google Scholar 

  17. Shearer C (2000) The CRISP-DM model: the new blueprint for data mining. J Data Warehous 5:13–22

    Google Scholar 

  18. http://www.rockwellautomation.com/global/products-technologies/motor-control-devices/overview.page? Accessed 20 October, 2015

  19. http://csegroups.case.edu/bearingdatacenter/pages/welcome-case-western-reserve-university-bearing-data-center-website. Accessed 10 July, 2015

  20. http://csegroups.case.edu/bearingdatacenter/pages/apparatus-procedures. Accessed 18 October, 2015

  21. Misti M, Misti Y, Oppnheim G, Poggi J (2002) Wavelet Toolbox User’s Guide, The Mathworks, Inc.

  22. Donoho DL (1995) De-noising by Soft-thresholding. IEEE Trans Inf Theory 41–31:613–627

    MathSciNet  Article  MATH  Google Scholar 

  23. Kenney JF, Keeping ES (1962) “The Standard Deviation” and “Calculation of the Standard Deviation.” §6.5-6.6 in Mathematics of Statistics, Pt. 1, 3rd edn. Van Nostrand, Princeton, pp 77–80

  24. Kenney JF, Keeping ES (1962) “Skewness.” §7.10 in Mathematics of Statistics, Pt. 1, 3rd edn. Van Nostrand, Princeton, pp 100–101

  25. Kenney JF, Keeping ES (1962) “Kurtosis.” §7.12 in Mathematics of Statistics, Pt. 1, 3rd edn. Van Nostrand, Princeton, pp 102–103

  26. Wong KT, Wang B, Chen J-C (2011) OFDM PAPR Reduction by Switching Null Subcarriers & Data-Subcarriers. Electron Lett 47(1):62–63

    Article  Google Scholar 

  27. Peng HW, Chiang PJ (2011) Control of mechatronics systems-ball bearing fault diagnosis using machine learning techniques. Kaohsiung, Taiwan

    Google Scholar 

  28. http://www.cs.waikato.ac.nz/ml/weka/index.html. Accessed 8 July 2015

  29. http://www.cs.waikato.ac.nz/ml/index.html. Accessed 8 July 2015

  30. Kenney JF, Keeping ES (1951) Rose and Smith 2002, pp 189–256

  31. Weisstein EW (2015) From MathWorld—a Wolfram Web Resource. http://mathworld.wolfram.com/k-Statistic.html. Accessed 4 July 2015

  32. Fawcett T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27: 861–874. http://en.wikipedia.org/wiki/Receiver_operating_characteristic

  33. Kankar PK, Sharma SC, Harsha SP (2011) Fault diagnosis of ball bearings using machine learning methods. Expert Syst Appl 38:1876–1886

    Article  Google Scholar 

  34. Li B, Zhang W (2011) Supervised Locally Linear Embedding Projection (SLLEP) for machinery fault diagnosis. Mech Syst Signal Process 25:3125–3134

    Article  Google Scholar 

  35. Breiman L (2001) Random Forests. Mach Learn 45(1):5–32

    MathSciNet  Article  MATH  Google Scholar 

  36. http://weka.8497.n7.nabble.com/How-does-Random-Forest-work-in-weka-td4863.html. Accessed 24 July 2015

  37. http://weka.sourceforge.net/doc.dev/weka/classifiers/trees/J48.html. Accessed 6 July 2015

  38. Witten I, Frank E (2011) Data mining: practical machine learning tools and techniques, 2nd edn, pp 198

  39. http://weka.sourceforge.net/doc.packages/simpleCART/weka/classifiers/trees/SimpleCart.html. Accessed 6 July 2015

  40. Farid DM, Harbi N, Rahman MZ (2010) Combining Naive Bayes and decision tree for adaptive intrusion detection. Int J netw Security appl (IJNSA) 2:12–25

    Article  Google Scholar 

  41. Iba W, Langley P (1992) Induction of One-Level Decision Trees. In: ML92: Proceedings of the Ninth International Conference on Machine Learning, Aberdeen, Scotland, 1–3 July 1992, Morgan Kaufmann, San Francisco, pp 233–240

  42. Devroye L, Kruszewski P (1996) The botanical beauty of random binary trees. In: Brandenburg FJ (ed) Graph Drawing: 3rd International Symposium, GD’95, Passau, Germany, September 20–22, 1995, Lecture Notes in Computer Science 1027, Springer-Verlag, pp 166–177

  43. http://weka.sourceforge.net/doc.dev/weka/classifiers/trees/REPTree.html. Accessed 6 July 2015

  44. Chang CC, Lin CJ, (2011) LIBSVM: a library for support vector machines. ACM Trans Intell Syst Technol 2

  45. Bhadeshia HKDH (1999) “Neural networks in materials science” (PDF). ISIJ Int 39(10):966–979

    Article  Google Scholar 

  46. Rosenblatt F (1961) Principles of Neurodynamics: Perceptrons and the Theory of Brain Mechanisms. Spartan Books, Washington DC. http://en.wikipedia.org/wiki/Multilayer_perceptron

  47. Webb GI, Boughton J, Wang Z (2005) Not So Naive Bayes: aggregating one-dependence estimators. Mach Learn (Springer) 58(1):5–24

    Article  MATH  Google Scholar 

  48. Castillo E, Gutiérrez JM, Hadi AS (1997) Learning Bayesian Networks. Expert Systems and Probabilistic Network Models. Monographs in computer science. Springer-Verlag, New York, pp 481–528

  49. Altman NS (1992) An introduction to kernel and nearest-neighbor nonparametric regression. Am Stat 46(3):175–185

    MathSciNet  Google Scholar 

  50. http://weka.sourceforge.net/doc.dev/weka/classifiers/lazy/KStar.html. Accessed 6 July 2015

  51. Du Z, Jeffrey JP  (2007) Advances in machine learning applications in software engineering, pp 253

  52. Fisher DL (1966) Data, documentation and decision tables. Commun ACM 9(1):26–31

    Article  Google Scholar 

  53. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273

    MATH  Google Scholar 

  54. Meyer Yves (1992) Wavelets and Operators. Cambridge University Press, Cambridge

    MATH  Google Scholar 

  55. Pearson K (1901) On Lines and Planes of Closest Fit to Systems of Points in Space (PDF). Philos Mag 2(11):559–572

    Article  MATH  Google Scholar 

  56. Eiben AE et al (1994) Genetic algorithms with multi-parent recombination. In: PPSN III: Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature, Jerusalem, Israel, pp 78–87

  57. Jolliffe IT (2002) Principal Component Analysis, Series: Springer Series in Statistics, 2nd edn, vol 487. Springer, NY, pp 28

  58. Ting CK (2005) On the Mean Convergence Time of Multi-parent Genetic Algorithms Without Selection. In: Advances in Artificial Life. Springer, Berlin, Heidelberg, pp 403–412

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nikos Katsifarakis.

Additional information

Technical Editor: Francisco Ricardo Cunha.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Katsifarakis, N., Riga, M., Voukantsis, D. et al. Computational intelligence methods for rolling bearing fault detection. J Braz. Soc. Mech. Sci. Eng. 38, 1565–1574 (2016). https://doi.org/10.1007/s40430-015-0458-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40430-015-0458-6

Keywords

  • Computational intelligence
  • Rolling bearings
  • Vibrations data
  • Aggregation
  • Experiment optimization