Analog circuit fault diagnosis method based on circular model and neural network

A technology for simulating circuit faults and neural networks, applied in the field of fault diagnosis of analog circuits, can solve problems such as difficult-to-storage faults, and achieve the effect of improving the fault diagnosis rate

Active Publication Date: 2019-10-08
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, the circle model under the influence of tolerance also has infinitely many specific eigenvalues, and it is difficult to fully store all faults by using the fault dictionary method

Method used

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  • Analog circuit fault diagnosis method based on circular model and neural network
  • Analog circuit fault diagnosis method based on circular model and neural network
  • Analog circuit fault diagnosis method based on circular model and neural network

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Embodiment

[0025] In order to better illustrate the technical contents and invention points of the present invention, the theoretical derivation process of the present invention will be described first.

[0026] figure 1 is the analog circuit diagram. Such as figure 1 As shown, N represents a linear time-invariant circuit, which consists of an independent voltage source excitation. Indicates the output voltage phasor on the selected measuring point, and x is a passive component. According to the substitution theorem, the passive element x can be replaced by an independent voltage source with the same voltage across its terminals, resulting in an equivalent circuit. figure 2 yes figure 1 Equivalent circuit diagram of the analog circuit shown. According to Thevenin's theorem, any active linear time-invariant port network can be equivalently replaced by a series branch of a voltage source and an impedance, so:

[0027]

[0028] in, yes figure 2 Open-circuit voltage phasor o...

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Abstract

The invention discloses an analog circuit fault diagnosis method based on a circular model and a neural network. Firstly, fuzzy group analysis is carried out on an analog circuit, and each piece of fuzzy group information is obtained; then circular model parameters of different measuring points of all the elements in the analog circuit under different test frequencies are obtained through circularmodel simulation, and a feature vector is obtained through construction to be used as a training sample for training a constructed neural network; and a circular model parameter of each measuring point at each test frequency is obtained according to degradation data during fault diagnosis, so as to form a test feature vector to be input into a trained neural network for fault diagnosis. Accordingto the method, the fault diagnosis of the analog circuit is realized by combining the circular model parameters with the neural network, so that the fault diagnosis rate of the analog circuit can beeffectively improved.

Description

technical field [0001] The invention belongs to the technical field of analog circuit fault diagnosis, and more specifically relates to an analog circuit fault diagnosis method based on a circle model and a neural network. Background technique [0002] With the rapid development of integrated circuits, in order to improve product performance, reduce chip area and cost, it is necessary to integrate digital and analog components on the same chip. According to data reports, although the analog part only accounts for 5% of the chip area, its fault diagnosis cost accounts for 95% of the total diagnostic cost. Analog circuit fault diagnosis has always been a "bottleneck" problem in the integrated circuit industry. At this stage, some relatively well-developed analog circuit fault diagnosis theories have been applied to practice, such as: fault dictionary method in pre-test analog diagnosis method, component parameter identification method and fault verification method in post-test...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01R31/28
CPCG01R31/2851
Inventor 杨成林周秀云黄建国
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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