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Fuzzy Control pp 397-405 | Cite as

Application of the FSOM to Machine Vibration Monitoring

  • Ingo Jossa
  • Uwe Marschner
  • Wolf-Joachim Fischer
Part of the Advances in Soft Computing book series (AINSC, volume 6)

Abstract

In this paper we are describing the application of the Fuzzy Self-Organizing Map (FSOM) and some feature extraction methods to a intelligent microsystem for online machine vibration monitoring. We have developed such a microsystem with some of the most advanced semiconductor and microsystem technologies. On the basis of these technologies it is possible to perform the complete condition monitoring and diagnosis stage directly in the microsystem which is applied to a observed machine.

Keywords

Vibration Signal Feature Extraction Method Winner Neuron Quartile Range Time Frequency Plane 
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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References

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    Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithmen. Plenum, New York (1981)CrossRefGoogle Scholar
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    Jossa, I., Marschner, U., Fischer, W.-J.: A Intelligent Microsystem for Machine Vibration Monitoring. Proceedings KES’99, Adelaide, Australia (1999)Google Scholar
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    Kohonen, T.: Self-Organizing Formation of Topologically Correct Feature Maps. Biol. Cyb. 43 (1982) 59–69MathSciNetMATHCrossRefGoogle Scholar
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    Kohonen, T.: Self-Organizing Maps. Springer, Berlin Heidelberg (1995)CrossRefGoogle Scholar
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    Tsao, E.C.-K., Bezdek, J.C., Pal, N.R.: Fuzzy Kohonen Clustering Networks. Pattern Recognition 27 (1994) 757–764CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Ingo Jossa
    • 1
  • Uwe Marschner
    • 1
  • Wolf-Joachim Fischer
    • 1
  1. 1.Semiconductor and Microsystems Technology LaboratoryDresden University of TechnologyDresdenGermany

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