Journal of Mechanical Science and Technology

, Volume 28, Issue 8, pp 2947–2952 | Cite as

Fault diagnosis of rotating machine by thermography method on support vector machine

Article

Abstract

Feature-based classification techniques consist of data acquisition, preprocessing, feature representation, feature calculation, feature selection, and classifiers. They are useful for online, real-time condition monitoring and fault diagnosis / features, which are now available with the development of information technologies and various measurement techniques. In this paper, an intelligent feature-based fault diagnosis is suggested, developed, and compared with vibration signals and thermal images. Fault diagnosis is performed using thermal imaging along with support vector machine (SVM) classification to simulate machinery faults, resulting in an accuracy level comparable to vibration signals. The observed results show that fault diagnosis using thermal images for rotating machines can be applied to industrial areas as a novel intelligent fault diagnostic method with plausible accuracy. It can be also proposed as a unique non-contact method to analyze rotating systems in mass production lines within a short time.

Keywords

Condition monitoring Fault diagnosis Rotating machine Support vector machine (SVM) Thermal image 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Y. Lei, Z. He and Y. Zi, Application of an intelligent classification method to mechanical fault diagnosis, Expert System with Application, 36 (2009) 9941.CrossRefGoogle Scholar
  2. [2]
    J. H Williams, A. Davies and P. R. Drake, Condition-based Maintenance and Machine Diagnostics, Chapman & Hall, London (1994).Google Scholar
  3. [3]
    A. Barber, Handbook of noise and vibration control, 6th Ed., Elsevier Advanced Technology Publications, UK (1992).Google Scholar
  4. [4]
    B. S. Yang, D. S. Lim and J. L. An, Vibration diagnostic system of rotating machinery using artificial neural network and wavelet transform, Proc. 13 th International Congress on COMADEM, Houston, USA (2000) 12–20.Google Scholar
  5. [5]
    A. K. S. Jardine, D. Lin and D. Banjevic, A review on machinery diagnostics and prognostics implementing conditionbased maintenance, Mechanical Systems and Signal Processing, 20 (2006) 1483.CrossRefGoogle Scholar
  6. [6]
    A. Widodo and B. S. Yang, Support vector machine in condition monitoring and fault diagnosis, Mechanical System and Signal Processing, 21 (2007) 2560.CrossRefGoogle Scholar
  7. [7]
    B. Samanta, Gear fault detection using artificial neural networks and support vector machines with genetic algorithms, Mechanical Systems and Signal Processing, 18 (3) (2004) 625.CrossRefMathSciNetGoogle Scholar
  8. [8]
    S. B. Glavatskih, O. Uusitalo and D. Spohn, Simultaneous monitoring of oil film thickness and temperature in fluid film bearings, Tribology International, 34 (12) (2001) 853.CrossRefGoogle Scholar
  9. [9]
    S. B. Glavatskih, A method of temperature monitoring in fluid film bearing, Tribology International, 37 (2) (2004) 143.CrossRefGoogle Scholar
  10. [10]
    P. De Choudhury and E. W. Barth, A comparison of film temperature and oil discharge temperature for a tilting-pad jounal bearing, J. lubrication Technology, 103 (1) (1981) 115.Google Scholar
  11. [11]
    A. Mazioud, L. Ibos, A. Khlaif and J. F. Durastant, Detection of rolling bearing degradation using infrared thermography, Proc. of 9 th International Conference on Quantitative Infrared Thermography, Krakow, Poland, July (2008).Google Scholar
  12. [12]
    A. M. Younus, A. Widodo and B. S. Yang, Evaluation of thermography image data for machine fault diagnosis, Nondestructive Testing and Evaluation, 25 (2009) 231.CrossRefGoogle Scholar
  13. [13]
    A. M. Younus and B. S. Yang, Intelligent fault diagnosis of rotating machinery using infrared thermal image, Expert System with Applications, 39 (2011) 2082.CrossRefGoogle Scholar
  14. [14]
    V. N. Vapnik, The nature of statistical learning theory, Springer, New York (1995).CrossRefMATHGoogle Scholar
  15. [15]
    C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, 20 (1995) 273.MATHGoogle Scholar
  16. [16]
    C. W. Hsu and C. J. Lin, A comparison of methods for multi-class support vector machines, IEEE Trans. Neural Networks, 13 (2002) 415.CrossRefGoogle Scholar
  17. [17]
    Colour Theory: Understanding and modelling colour, (http://www.jiscdigitalmedia.ac.uk/stillimages/advice/colour-theory-understanding-and-modelling-colour/).Google Scholar
  18. [18]
    CIEL*a*b* Color Scale, Hunterlab application Notes, 8 7 (2008).Google Scholar

Copyright information

© The Korean Society of Mechanical Engineers and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Interdisciplinary Program of Acoustics and Vibration EngineeringPukyong National UniversityBusanKorea
  2. 2.Department of Naval Architecture and Marine Systems EngineeringPukyong National UniversityBusanKorea
  3. 3.Department of Mechanical EngineeringInha UniversityIncheonKorea

Personalised recommendations