Abstract

The Kernel Principal Component Analysis (KPCA) extracts the principal components by computing the population variance, which doesn’t consider the difference between one class and the others. So, it makes against the fault diagnosis. For solving this problem, the study introduced Fisher classification function into The KPCA and proposed an improved FKPCA with the class information. Then, the algorithm was applied in analog-circuit fault feature extraction and the neural network was applied to diagnose the faults. The results indicate the classification effect of the principal components extracted by the algorithm is more better. It improves the rate of fault diagnosis and reduces the test time.

Keywords:

Between-class scatter matrix, feature extraction, fisher criterion, KPCA, within-class scatter matrix,

References

Competing interests

The authors have no competing interests.

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Copyright

The authors have no competing interests.