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Fault Diagnosis Method of Analog Circuit Based on Circle Model and Neural Network

An analog circuit fault and neural network technology, applied in the field of analog circuit fault diagnosis, can solve problems such as hard-to-storage faults, and achieve the effect of improving the fault diagnosis rate

Active Publication Date: 2020-09-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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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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  • Fault Diagnosis Method of Analog Circuit Based on Circle Model and Neural Network
  • Fault Diagnosis Method of Analog Circuit Based on Circle Model and Neural Network
  • Fault Diagnosis Method of Analog Circuit Based on Circle 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. like 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 of p...

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Abstract

The invention discloses an analog circuit fault diagnosis method based on a circle model and a neural network. First, the fuzzy group analysis is performed on the analog circuit to obtain the information of each fuzzy group, and then the parameters of each component in the analog circuit under different test frequencies are obtained through the circle model simulation. The circle model parameters of different measurement points are constructed to obtain the feature vector, which is used as a training sample to train the constructed neural network, and the circle model parameters of each measurement point at each test frequency are obtained according to the degradation data during fault diagnosis, which constitutes the input of the test feature vector Trained neural network for fault diagnosis. The invention realizes the analog circuit fault diagnosis by combining the circle model parameters and the neural network, and can effectively improve the fault diagnosis rate of the analog circuit.

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 Patents(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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