Fault Severity Estimation in Rotating Mechanical Systems Using Feature Based Fusion and Self-Organizing Maps

  • Dimitrios Moshou
  • Dimitrios Kateris
  • Nader Sawalhi
  • Spyridon Loutridis
  • Ioannis Gravalos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6353)

Abstract

The capability of Self-Organizing Maps (SOM) to visualize high- dimensional data is well known. The presented work concerns a SOM based diagnostic system architecture for the monitoring of fault evolution in bearings. Bearings form an essential part of rotating machinery and their failure is one of the most common causes of machine breakdowns. A SOM based approach has been used to map time series of feature data produced by acceleration sensors in order to capture the process dynamics. The fusion of specific features and the introduction of new features related to fault severity can enable the monitoring of fault evolution. The evolution of system states showing the bearing health trend has been shown to warn of impeding failure.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Xu, Z., Xuan, J., Shi, T., Wu, B., Hu, Y.: Application of a modified fuzzy ARTMAP with feature-weight learning for the fault diagnosis of bearing. Expert Systems with Applications 36, 9961–9968 (2009)CrossRefGoogle Scholar
  2. 2.
    Baydar, N., Ball, A.: A comparative study of acoustic and vibration signals in detection of gear failures using Wigner–Ville distribution. Mechanical Systems and Signal Processing 15(6), 1091–1107 (2001)CrossRefGoogle Scholar
  3. 3.
    Wang, C., Gao, R.X.: Wavelet transform with spectral post-processing for enhanced feature extraction. IEEE Transactions on Instrumentation and Measurement 52(4), 1296–1301 (2003)CrossRefGoogle Scholar
  4. 4.
    Randall, R.B.: Applications of Spectral Kurtosis in Machine Diagnostics and Prognostics. In: Ostachowicz, W.M., Dulieu-Barton, J.M., Holford, K.M., Krawczuk, M., Zak, A. (eds.) Journal Key Engineering Materials. Damage Assessment of Structures VI, vols. 293-294, pp. 21–32 (2005)Google Scholar
  5. 5.
    Estupiñan, E., White, P., San Martin, C.: A Cyclostationary Analysis Applied to Detection and Diagnosis of Faults in Helicopter Gearboxes. In: Rueda, L., Mery, D., Kittler, J. (eds.) CIARP 2007. LNCS, vol. 4756, pp. 61–70. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  6. 6.
    Kohonen, T.: Self-organizing Maps. Springer, Berlin (2001)MATHGoogle Scholar
  7. 7.
  8. 8.
  9. 9.
    Huanga, R., Xia, L., Lib, X., Liuc, C.R., Qiud, H., Leed, J.: Residual life predictions for ball bearings based on self-organizing map and backpropagation neural network methods. Mechanical Systems and Signal Processing 21(1), 193–207 (2007)CrossRefGoogle Scholar
  10. 10.
    Heng, A., Zhang, S., Tan, A.C.C., Mathew, J.: Rotating machinery prognostics: State of the art, challenges and opportunities. Mechanical Systems and Signal Processing 23(3), 724–739 (2009)CrossRefGoogle Scholar
  11. 11.
    Yang, J., Zhang, Y., Zhu, Y.: Intelligent fault diagnosis of rolling element bearing based on SVMs and fractal dimension. Mechanical Systems and Signal Processing 21(5), 2012–2024 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Dimitrios Moshou
    • 1
  • Dimitrios Kateris
    • 1
  • Nader Sawalhi
    • 2
  • Spyridon Loutridis
    • 3
  • Ioannis Gravalos
    • 3
  1. 1.Agricultural Engineering LaboratoryAristotle UniversityThessalonikiGreece
  2. 2.School of Mechanical and Manufacturing EngineeringThe University of New South WalesSydneyAustralia
  3. 3.Departments of Electrical Engineering and Biosystems EngineeringTechnological Educational Institute of LarissaLarissaGreece

Personalised recommendations