Automated diagnostic approaches for deffective rolling element bearing using minimal training pattern classification methods

  • K. C. Gryllias
  • C. Yiakopoulos
  • I. Antoniadis
Conference paper


Rolling Element Bearings consist one of the most widely used industrial machine elements, being the interface between the stationary and the rotating part of the machine. Due to their importance a plethora of monitoring methods and fault diagnosis procedures have been developed, in order to reduce maintenance costs, improve productivity, and prevent malfunctions and failures during operation which could lead to the downtime of the machine. Towards this direction, among different automatic diagnostic methods, the Support Vector Machine (SVM) method has been shown to present a number of advantages. Support Vector Machine is a relatively new computational learning method based on Statistical Learning Theory and combines fundamental concepts and principles related to learning, well-defined formulation and self-consistent mathematical theory. The key aspects about the use of SVMs as a rolling element bearing health monitoring tool are the lack of actual experimental data, the optimal selection of the type and the number of input features, and the correct selection of the kernel function and its corresponding parameters. A large number of input features have been proposed, being divided in two big categories: A) Traditional signal statistical features in the time domain, such as mean value, rms value, variance, skewness, kurtosis etc, B) Frequency domain based indices, such as energy values obtained at characteristic frequency bands of the measured and the demodulated signals. In this paper, the structure and the performance of a Support Vector Machine based approach for rolling element bearing fault diagnosis is presented. The main advantage of this method is that the training of the SVM is based on a model describing the dynamic behavior of a defective rolling element bearing, enabling thus the direct application of the SVM to experimental measurements of defective bearings, without the need of training the SVM with experimental data of a defective bearing.


Support Vector Machine Fault Diagnosis Simulated Signal Rolling Element Rolling Element Bearing 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Burges, C.J.C. (1998) A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2, 955–974.CrossRefGoogle Scholar
  2. 2.
    Gunn, S.R. (1998) Support vector machines for classification and regression. Technical report. University of Southampton. Department of Electrical and Computer Science.Google Scholar
  3. 3.
    Hu, Q., He, Z., Zhang, Z. & Zi, Y. (2007) Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble. Mechanical Systems and Signal Processing 21, 688-705.Google Scholar
  4. 4.
    Jack, L.B. & Nandi, A.K. (2001) Support vector machines for detection and characterisation of rolling element bearing faults. Proceedings of Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 215, 1065–1074.CrossRefGoogle Scholar
  5. 5.
    Jack, L.B. & Nandi, A.K. (2002) Fault detection using support vector machines and artificial neural networks, augmented by genetic algorithms. Mechanical Systems and Signal Processing 16, 373–390.CrossRefGoogle Scholar
  6. 6.
    Rojas, A. & Nandi, A. (2006) Practical scheme for fast detection and classification of rolling-element bearing faults using support vector machines. Mechanical Systems and Signal Processing 20, 1523-1536.CrossRefGoogle Scholar
  7. 7.
    Samanta, B., Al-Balushi, K.R. & Al-Araimi, S.A. (2003) Artificial neural networks and support vector machines with genetic algorithm for bearing fault detection. Engineering Applications of Artificial Intelligence 16, 657–665.CrossRefGoogle Scholar
  8. 8.
    Samanta, B. & Nataraj, C. (2009) Use of particle swarm optimization for machinery fault detection, Engineering Applications of Artificial Intelligence 22, 308-316.CrossRefGoogle Scholar
  9. 9.
    Yuan, S.-F. & Chu, F.-L. (2007a) Fault diagnosis based on support vector machines with parameter optimisation by artificial immunisation algorithm. Mechanical Systems and Signal Processing 21, 1318–1330.CrossRefGoogle Scholar
  10. 10.
    Yuan, S.-F. & Chu, F.-L. (2007b) Fault diagnosis based on particle optimisation and support vector machines. Mechanical Systems and Signal Processing 21, 1787–1798.CrossRefGoogle Scholar
  11. 11.
    Yang, B.-S., Han, T. & Hwang, W.-W. (2005) Fault Diagnosis of Rotating Machinery based on Multi-Class Support Vector Machines. Journal of Mechanical Science and Technology 19(3), 846-859.CrossRefGoogle Scholar
  12. 12.
    Yang, J., Zhang, Y. & Zhu, Y. (2007) Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mechanical Systems and Signal Processing 21, 2012-2024.Google Scholar
  13. 13.
    Antoni, J. & Randall, R. B. (2002) Differential diagnosis of gear and bearing faults. Transactions of the ASME. Journal of Vibration and Acoustics 124, 165-171.CrossRefGoogle Scholar
  14. 14.
    McFadden, P. D. & Smith, J. D. (1984) Model for the vibration produced by a single point defect in a rolling element bearing. Journal of Sound and Vibration 96, 69-82.CrossRefGoogle Scholar
  15. 15.
    Vapnik, V. (1995) The Nature of Statistical Learning Theory, Springer-Verlag, New York.MATHGoogle Scholar
  16. 16.
    Vapnik, V. (1998) Statistical Learning Theory, John Wiley and Sons, Inc., New York.MATHGoogle Scholar
  17. 17.
    Christianini, N. & Shawe-Taylor, J. (2000) An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press.Google Scholar
  18. 18.
    Loparo, K. A., Bearings vibration data set. Case Western Reserve University. Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • K. C. Gryllias
    • 1
  • C. Yiakopoulos
    • 1
  • I. Antoniadis
    • 1
  1. 1.Dynamics & Structures LaboratorySchool of Mechanical Engineering, Machine Design and Control Systems SectionAthensGreece

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