Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors

@article{Widodo2007ApplicationON,
  title={Application of nonlinear feature extraction and support vector machines for fault diagnosis of induction motors},
  author={Achmad Widodo and Bo-Suk Yang},
  journal={Expert Syst. Appl.},
  year={2007},
  volume={33},
  pages={241-250}
}
Recently, principal components analysis (PCA) and independent components analysis (ICA) was introduced for doing feature extraction. PCA and ICA linearly transform the original input into new uncorrelated and independent features space respectively. In this paper, the feasibility of using nonlinear feature extraction is studied and it is applied in support vector machines (SVMs) to classify the faults of induction motor. In nonlinear feature extraction, we employed the PCA and ICA procedure and… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 14 references

Application of multi-class support vector machines for fault diagnosis of rotating machinery

  • B. S. Yang, T. Han, W. W. Hwang
  • Journal of Mechanical Science and Technology,
  • 2005

ICALAB for signal processing; Toolbox for ICA, BSS and BSE. Available from http://www.bsp.brain.riken.jp/ICALAB/ICALABSignalProc

  • A. Cichocki, S. Amari, K. Siwek, T. Tanaka
  • 2004

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