Analogical electronic circuit fault diagnostic method based on M-ary-structure classifier

Abstract

The invention discloses an analogical electronic circuit fault diagnostic method based on an M-ary-structure classifier, which belongs to the field of analogical circuit network test. The invention comprises the following steps: firstly, analyzing the testability of an analogical circuit to determine proper test stimulus and a test point; secondly, acquiring to-be-tested outputting signals at a testable node of an electronic circuit; thirdly, compressing acquired circuit fault information to extract fault characteristic samples; finally, designing the tags of the samples in an M-ary encoding way, establishing a hyperspherical sub-classifier in the way of a SVDD classifier, training the samples, and storing information to form a fault dictionary after training. The invention has the advantages of less classifiers, simple method, high reliability, and the like, thereby improving the automation degree and the efficiency of the online fault diagnosis of a power electronic circuit.

Description

technical field [0001] The invention relates to an analog electronic circuit fault diagnosis method based on an M-ary structure classifier, which belongs to the field of analog circuit network testing. Background technique [0002] The diagnostic technology of analog circuit has become the third branch after network analysis and network synthesis. Among them, the simulation diagnosis method based on artificial intelligence has become one of the research hotspots in the world because of its many advantages. At present, the mainstream of intelligent fault diagnosis methods for analog circuits is the pattern recognition method based on machine learning. [0003] Pattern recognition methods based on machine learning can be divided into two categories: supervised learning and unsupervised learning. In analog circuit diagnosis, most methods are based on supervised learning or hybrid integration of various methods. Among various supervised learning methods, the support vector ma...

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Problems solved by technology

[0004] Using support vector machine classifiers has many advantages, but commonly used support vector machine classifiers use hypersurfaces to segment two types of samples in high-dimensional spaces. When this method is used in fault diagnosis of some analog electronic circuits, the original samples are not considered. The distribution attributes and characteristics (for example: two types of samples may be distributed in the form of hyperspheres or approximate hyperspheres), which may increase the misdiagnosis rate of samples

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