Feature Extraction Method for Fault Diagnosis of Machine Unit Based on Wavelet Singularity Principle and Immunology Optimization Principle

  • Jian CenEmail author
  • Yinbo Wu
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 133)


Consider on fault signal coupling of machine unit for fault feature extraction caused by difficult problems, wavelet singularity theory be used complex fault feature extraction. Fault signal after wavelet denosing, which use clonal selection for fault classification. In the new feature space, the characteristics of different types of fault modes enhanced data aggregation, the different fault can be divided, and the compound fault signal will be separated, thereby enhancing the accuracy of fault diagnosis, fault samples of analog complex machine unit be trained, detected, and diagnosed, results to be verified.


wavelet singularity principle clonal selection fault diagnosis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chen, C., Hu, l., Zhou, B., Fei, C.: Equipment vibration analysis and fault diagnosis tenchnology, pp. 496–498. Science Press, Beijing (2007)Google Scholar
  2. 2.
    Xu, C., Li, G.: Practical wavelet method. Huazhong Science and Technology University Press, Wuhan (2001)Google Scholar
  3. 3.
    Ma, H.: Motor status detection and fault diagnosis. Machinery Industry Press, Beijing (2007)Google Scholar
  4. 4.
    Aydin, I., Karakose, M., Akin, E.: Generation of Classification Rules using Artificial Immune System for Fault Diagnosis. In: IEEE International Conference on Systems Man and Cybernetics, Istanbul, pp. 343–349 (2010)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of AutomationGuangdong Polytechnic Normal UniversityGuangzhouChina

Personalised recommendations