Fault Diagnosis Method of Switching Current Circuit Based on Wavelet Fractal and Kernel Principal Component Features
A switching current and circuit fault technology, which is applied in the field of fault diagnosis of switching current circuits based on wavelet fractal and kernel principal component features, can solve the problems of low fault classification rate and no solution, and achieve the effect of high fault classification rate
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[0040] Embodiment 1: The fault diagnosis of the switching current circuit is aimed at catastrophic (hard) and parametric (soft) faults. Compared with soft faults, hard faults are relatively easy to diagnose. figure 1 The soft fault diagnosis of the 6th-order Chebyshev low-pass filter circuit shown in the figure shows the normalized transconductance values of the transistors in the figure: Ma=1, Mb=0.4255, Mc=1.9845, Md=0.3455, Me=0.9845 , Mf=0.5827, Mg=1.9134, Mh=0.085, Mi=0.8577, Mj=2.1021, Mk=0.2787. The cutoff frequency of the circuit is 5MHz, the ratio of the cutoff frequency to the clock frequency is 1:4, the clock frequency is 20MHz, and the in-band ripple is 0.5dB. The tolerance range of transconductance gm is 5% respectively, and there are 5 transistors that may fail in the circuit, namely Mg, Mf, Me, Md and Mj. When the gm value of one of the transistors deviates from the nominal value by 50%, while the other four transistors vary within their tolerance range, the...
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