Abstract
Correct incipient identification of an analog circuit fault is conducive
to the health of the analog circuit, yet very difficult. In this paper, a
novel approach to analog circuit incipient fault identification is
presented. Time responses are acquired by sampling outputs of the circuits
under test, and then the responses are decomposed by the wavelet transform
in order to generate energy features. Afterwards, lower-dimensional
features are produced through the kernel entropy component analysis as
samples for training and testing a one-against-one least squares support
vector machine. Simulations of the incipient fault diagnosis for a
Sallen-Key band-pass filter and a two-stage four-op-amp bi-quad low-pass
filter demonstrate the diagnosing procedure of the proposed approach, and
also reveal that the proposed approach has higher diagnosis accuracy than
the referenced methods.
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