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Gear Crack Detection Using Kernel Function Approximation

  • Weihua Li
  • Tielin Shi
  • Kang Ding
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

Failure detection in machine condition monitoring involves a classification mainly on the basis of data from normal operation, which is essentially a problem of one-class classification. Inspired by the successful application of KFA (Kernel Function Approximation) in classification problems, an approach of KFA-based normal condition domain description is proposed for outlier detection. By selecting the feature samples of normal condition, the boundary of normal condition can be determined. The outside of this normal domain is considered as the field of outlier. Experiment results indicated that this method can be effectively and successfully applied to gear crack diagnosis.

Keywords

Feature Vector Fault Diagnosis Kernel Principal Component Analysis Support Vector Data Description Machine Condition Monitoring 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Weihua Li
    • 1
  • Tielin Shi
    • 2
  • Kang Ding
    • 1
  1. 1.School of Automotive EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Mechanical Science and EngineeringHuazhong University of Science and TechnologyWuhanChina

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