Tolerance analog circuit fault diagnosing method based on wavelet transform and fractal dimension

Abstract

The invention provides a tolerance analog circuit fault diagnosing method based on wavelet transform and fractal dimension, which comprises the following steps of: aiming at the problem of analog circuit error diagnosis brought by element tolerance, circuit noise and measurement error, carrying out noise elimination processing on a response signal of a circuit by using wavelet transform; aiming at the problem that a transverse shaft and a longitudinal shaft of a sampling signal of an analog circuit are two physical quantities, improving the accuracy of fault diagnosis by using a method for respectively uniformizing the transverse shaft and the longitudinal shaft; aiming at the problem of error diagnosis caused by characteristic overlapping which is probably generated by directly extracting a wavelet decomposed coefficient as the fault characteristic, calculating a box dimension as the fault characteristic by using a wavelet decomposed signal; and inputting the characteristics into a BP (Back Propagation) network for carrying out fault positioning, wherein the adjustment of BP network weight is realized in a batch processing way. By adopting the invention, the speed and accuracy offault diagnosis can be improved.

Description

technical field [0001] The invention relates to a tolerance analog circuit fault diagnosis method based on wavelet transform and fractal dimension. technical background [0002] Since the start of research on analog circuit fault diagnosis technology in the 1960s, many achievements have been made. Researchers have proposed many methods, among which the artificial neural network is closer to the human brain in terms of composition principles and functional characteristics, and is different from traditional fault diagnosis methods. Compared with the analysis method, it does not need to establish an accurate mathematical model of the object, and avoids complicated mathematical operations, so it has been more and more widely used in fault diagnosis. [0003] The extraction of fault features plays a very important role when neural network is used for fault pattern recognition. Fractal theory is a science that takes irregular things as the research object, and has been widely use...

AI Technical Summary

Problems solved by technology

Fractal theory is a science that takes irregular things as the research object, and has been widely used in the sudden fault diagnosis of dynamic systems such as machinery and electric power. However, there are few reports of applying it to analog circuit fault diagnosis.

Related literatures apply fractal theory to the extraction of fault features of analog circuits, and achieve good results. However, since the horizontal axis of the sampling signal of analog circuits is time or frequency, and the vertical axis is voltage signal or transfer function amplitude, this is Two different physical quantities. Therefore, if the signal is not processed and the method of square grid coverage is directly used to calculate the box dimension as the fault feature, the diagnostic accuracy may be reduced or even misdiagnosed.

Considering the influence of circuit tolerance and noise, if the signal extracted from the circuit under test is directly calculated as a feature, it is difficult to meet the actual needs of the project

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