A Novel Approach for Analog Circuit Incipient Fault Diagnosis by Using Kernel Entropy Component Analysis as a Preprocessor

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Abstract: In order to diagnose incipient fault of analog circuits effectively, an analog circuit incipient fault approach by using kernel entropy component analysis (KECA) as a preprocessor is proposed in the paper. Time responses are acquired by sampling outputs of the circuits under test. Raw features with high dimension are generated by wavelet transform. Furthermore, lower dimensional features are produced through KECA as samples which are used to construct a classification model based on least squares support vector machine. Bandpass filter and leapfrog filter incipient fault diagnosis simulations demonstrate the diagnose procedure of the proposed approach, and also validate proposed approach by using KECA as a preprocessor can produce higher diagnosis accuracy than the commonly used methods.

WSEAS Transactions on Circuits and Systems, ISSN / E-ISSN: 1109-2734 / 2224-266X, Volume 15, 2016, Art. #3

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Chao-Long Zhang, Yi-Gang He, Li-Fen Yuan, Wei He, "A Novel Approach for Analog Circuit Incipient Fault Diagnosis by Using Kernel Entropy Component Analysis as a Preprocessor," WSEAS Transactions on Circuits and Systems, vol. 15, pp. 13-20, 2016, DOI:
Chao-Long Zhang, Yi-Gang He, Li-Fen Yuan, Wei He. A Novel Approach for Analog Circuit Incipient Fault Diagnosis by Using Kernel Entropy Component Analysis as a Preprocessor. WSEAS Transactions on Circuits and Systems. 2016;15:13-20.
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