Method for realizing analogue circuit fault diagnosis based on standard deviation and skewness by neural network

Abstract

The invention discloses a method for realizing analogue circuit fault diagnosis based on standard deviation and skewness by a neural network, which comprises the following steps of: 1) for a circuit under the condition that elements have no tolerance, performing AC analysis in various fault states, extracting amplitude-frequency characteristics and calculating the standard deviation and the skewness to obtain a sample without the tolerance; 2) for the circuit under the condition that the elements have tolerance, performing the AC analysis and Monte Carlo analysis in the various fault states, extracting the amplitude-frequency characteristics and calculating the standard deviation and the skewness to obtain the sample with the tolerance; 3) when a certain element parameter of the circuit is increased from zero to infinity, performing parameter scanning analysis, calculating a track between the standard deviation and the skewness and finding fault characteristic rules; and 4) forming a characteristic vector by the standard deviation and the skewneess obtained in the steps 1) to 3), inputting the characteristic vector into the neural network and performing fault classification. The method has the advantages of avoiding spectrum aliasing, simplifying the structure of the neural network and correctly differentiating the tolerance admissible range state, the soft fault state and the hard fault state of the circuit, along with short network training time and high fault positioning accuracy.

Description

technical field [0001] The invention relates to a neural network method for analog circuit fault diagnosis, in particular to a neural network method for analog circuit fault diagnosis based on standard deviation and skewness. Background technique [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. The relevant literature o...

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Problems solved by technology

However, it is precisely this unified description of fault characteristics that makes it impossible for these methods to distinguish whether the circuit component parameters are within the tolerance range or a fault has occurred, nor can it dist

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