Fault Diagnosis of Analog Circuit Using Spectrogram And LVQ Neural Network

This paper addresses a refined fault feature problem of analog circuit using a feature extraction technique based on auditory feature. The proposed approach applies short-time fernier transform (STFT) to obtain the time and frequency features of the fault responses being indicated separately by the cross and vertical axes in a spectrogram, which gives much more refined description of the fault behavior. To reduce the computational complexity derived from the high-dimensional texture features embedded in the spectrogram, the fault spectrograms are further processed by local binary patterns (LBP) operator for obtaining low-dimensional fault features. Completing the parameter settings of the network, the LBP feature vectors are fed to the learning vector quantization (LVQ) neural network for fault classification. The numerical experiments about an active high-pass filter are carried out to indicate our approach has an acceptable diagnostic rate with high accuracy.