Study On Feature Extraction Of Fault Circuits Based On Wavelet Transform

There are many classical methods to diagnose the fault in the circuits, but they are mostly in the condition such hard fault as the opened circuits and shorted circuits, they are difficult in the diagnosis on the soft fault generated by the changes of the parameters or the deficiencies of the circuits, especially for the more complicated IC. It can not complete the analysis and diagnosis of the soft fault signal. The classical analysis method for the signal is the Fourier analysis, which can get the characters of the signal by transforming the signal from the time scope to the frequency scope. But it can not make good analysis of the soft fault signal in the circuits which is random and unstable or can not reflect and extract the transient characters of the soft fault signal.Whereas the research of this paper is to give the preprocessing method based on wavelet transformation in the extraction of the soft fault signal of the IC. The tech of the wavelet transformation, as the development of the Fourier analysis and short time Fourier analysis, inherit the goods of the Fourier analysis and make up for the deficiencies of the Fourier analysis. It has a higher frequency resolution and a lower time resolution in the low frequency part and a lower frequency resolution and a higher time resolution in the high frequency part. In the meantime, it has the character of the multi-resolution and can express the partial signal characters. Because of this character, wavelet transformation can express the signal in all kinds of frequencies. It can decompose the signal into two parts: the approximation signal in the high frequency and the detail signal in the low frequency. It can also express the partial characters of the soft fault signal and diagnose the transient characters and edges of the signal.The paper is arranged in two parts: analysis of the tolerance for the circuits and the analysis of the soft trouble detection algorithm. Firstly, emulate the circuits with PSpice, which is a classical computer emulating software for the circuits. Analyze the normal signal and the soft trouble signal through the Mont Carlo method and make them the objects to be processed. Secondly, study the wavelet transform algorithm. Choose the wavelet base function according to the features of the sampled signal and the emulating analysis request. Based on the selection of the wavelet base function, we chose the general decomposing scale and proposed a soft fault signal analysis and extraction method with db2 mother wavelet function and a 6-layer decomposing scale. In this paper, applying the improved multi-resolution wavelet analysis method on the soft fault signal and got the approximate decomposed coefficients and the detailed coefficients of the signal. Compared with the normal signal, we can see the singularity features of the soft fault signal resulted from the changed parameters of the circuits components corresponding to the circuit frequency. The soft fault signal characters in the output of the circuit were extracted successfully, which solve the difficulty of the traditional Fourier analysis. Further more it provides a convenient base for diagnosing the fault in the circuit.In this paper the proposed method works better for analyzing the soft fault problems in the circuits, which can detect the problem components of the circuits, however, our method cannot detect all the troubled components in the circuits. Because not all the components are related to the frequency character of the circuits. The fault components of the circuits that are unrelated to the frequency of the circuits can be detected by the general fault dictionary method. Our proposed method in this paper can be used in the soft fault diagnose of the IC and will be further developed as the increasing development of the integrated circuits.