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Analog circuit early fault diagnosis method based on semi-supervised collaborative training

An analog circuit and collaborative training technology, which is applied in the field of early fault diagnosis of analog circuits based on semi-supervised collaborative training, can solve the problem that the source of the fault cannot be detected quickly, save the cost of marking, reduce the introduction of noise, guarantee The effect of accuracy

Active Publication Date: 2019-11-26
CHONGQING UNIV
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  • Application Information

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Problems solved by technology

[0005] In addition, the faults of the analog circuit are diversified, and the source of the fault cannot be checked quickly

Method used

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  • Analog circuit early fault diagnosis method based on semi-supervised collaborative training
  • Analog circuit early fault diagnosis method based on semi-supervised collaborative training
  • Analog circuit early fault diagnosis method based on semi-supervised collaborative training

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Embodiment Construction

[0018] In order to understand the present invention more clearly, below in conjunction with accompanying drawing, describe in detail:

[0019] refer to figure 1 , this embodiment includes the following steps:

[0020] 1) First collect the known fault signals and normal signals of the analog circuit, perform Fourier transform on the signals to obtain the spectrum diagram, and divide the spectrum by a specific algorithm to obtain a set of boundary lines of the spectrum.

[0021] First find all the local maxima of the spectrum, arrange all the maxima in descending order and normalize them to 0 to 1. The largest maximum value is M 1 , the minimum maximum value is M M , and then define the threshold as T=M M +α(M 1 -M M ), α∈(0,1). After the α value is given, the number of local maxima greater than the threshold is recorded as N, and the first N local maxima are taken, and the two are added and divided by 2 to obtain the boundary.

[0022] 2) According to the demarcation li...

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Abstract

The invention discloses a semi-supervised collaborative training analog circuit early fault diagnosis method, which can classify and identify analog circuit faults without using a large amount of labeled data so as to realize early fault diagnosis. The method comprises the following steps: firstly, carrying out Fourier transform on a known analog circuit normal signal and a fault signal; carryingout spectrum partitioning, empirical wavelet transform on the frequency spectrum; obtaining AM-FM components, carrying out hilbert transformation on the data; obtaining a plurality of EWF components;superposing the energy of the plurality of EWF components; finally, through a semi-supervised collaborative training algorithm, utilizing a small amount of labeled data fully for learning and utilizing a large amount of unlabeled data fully, thus the accuracy of marking actions is guaranteed to a certain extent, a large amount of marking cost is saved, and therefore early fault diagnosis of theanalog circuit based on semi-supervised collaborative training is achieved.

Description

technical field [0001] The invention belongs to the field of early fault diagnosis of analog circuits based on semi-supervised cooperative training, and relates to a method for early fault diagnosis of analog circuits based on semi-supervised cooperative training. Background technique [0002] Analog circuits are widely used in industrial production, daily life and high-tech fields, especially in military fields such as aerospace. In electronic circuit systems, early signs of failure can be difficult to discern, seriously affecting system performance. [0003] The faults of analog circuits can be divided into two types: hard faults and soft faults. Hard faults refer to "qualitative" changes in the parameters of components in the circuit, which lead to serious system failures, such as open circuit, short circuit, and component damage. Soft faults usually refer to the gradual deviation of component parameters from their tolerance range under the influence of time and environm...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G01R31/316
CPCG01R31/316G06F2218/08G06F2218/12G06F18/214G06F18/295Y04S10/52
Inventor 屈剑锋曹珊珊吴冬冬房晓宇李豪
Owner CHONGQING UNIV
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