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Fault diagnosis method of analog circuit based on random sinusoidal signal test and hmm

A technology for simulating circuit faults and sinusoidal signals, applied in the direction of analog circuit testing, electronic circuit testing, etc., can solve the problems of affecting the output response of the circuit, affecting the diagnosis results, limited frequency components, etc., to increase the degree of information, improve the diagnosability, The effect of improving accuracy

Inactive Publication Date: 2017-04-19
NANJING AGRICULTURAL UNIVERSITY
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In order to obtain a specific test signal, it is necessary to determine the parameters of the test signal. The final test signal has uncertainty, and the test signal will affect the output response of the circuit and directly affect the diagnosis result.
Second, deterministic signals contain limited frequency components

Method used

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  • Fault diagnosis method of analog circuit based on random sinusoidal signal test and hmm
  • Fault diagnosis method of analog circuit based on random sinusoidal signal test and hmm
  • Fault diagnosis method of analog circuit based on random sinusoidal signal test and hmm

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Experimental program
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Effect test

Embodiment 1

[0032] Embodiment 1, combining figure 1 , an analog circuit fault diagnosis method based on random sinusoidal signal testing and HMM, including the following steps:

[0033] A. Use random sinusoidal signal X(t)={x 1 (t),x 2 (t),...,x n (t)} excites the analog circuit under test, x n (t) amplitude, phase and frequency satisfy Gaussian distribution;

[0034] B. Gather the output data sample Y(t)={y of the analog circuit to be tested 1 (t),y 2 (t),...,y n (t)}, extract the time-domain features and spectral features of the output data samples to form feature components, and each type of feature component is a time series; where the time-domain feature component is the mathematical expectation m Y (t), variance Correlation coefficient R Y (τ), the spectral characteristic component is the power spectrum S Y (ω);

[0035] C1. Combined figure 2 , the four types of feature components are input into four HMMs as four types of time series, and four hidden Markov diagnostic ...

Embodiment 2

[0041] Embodiment 2, as the analog circuit fault diagnosis method based on random sinusoidal signal test and HMM of embodiment 1, in step A, random sinusoidal signal X (t) obtains as follows:

[0042] A1. Random sinusoidal signal X(t) satisfies X(t)=A(t)cos[Ω(t)t+Φ(t)], where A(t), Ω(t) and Φ(t) are respectively For amplitude random variables, phase random variables and frequency random variables that satisfy the Gaussian distribution;

[0043] A2. Use simulation software to generate samples of n groups of random sinusoidal signals, denoted as X(t)={x 1 (t),x 2 (t),...,x n (t)}, each sample signal x n (t) Excite the analog circuit under test.

Embodiment 3

[0044] Embodiment 3, as the analog circuit fault diagnosis method based on random sinusoidal signal test and HMM of embodiment 1 or 2, the mathematical expectation m described in step B Y (t), variance Correlation coefficient R Y (τ), power spectrum S Y (ω) is based on the stochastic signal analysis theory and obtained by mathematical statistics method:

[0045]

[0046]

[0047]

[0048]

[0049] where Y(t) is a random process, f Y (y,t) is the one-dimensional probability density function of Y(t), f Y (y 1 ,y 2 ,t 1 ,t 2 ) is the binary probability density function of Y(t), Y(t 1 ) and Y(t 2 ) respectively at t 1 and t 2 The random variable obtained by observing Y(t) at all times, τ is t 1 and t 2 interval between moments.

[0050] In Embodiments 1-3, the hidden Markov model based on random time series is implemented in the following way:

[0051] First, 4 HMM diagnostic models are trained with 4 kinds of feature sequences. HMM can be described by...

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Abstract

The invention discloses an analog circuit fault diagnosis method based on a random sinusoidal signal test and an HMM (Hidden Markov Model). The analog circuit fault diagnosis method comprises the following steps: A. stimulating a to-be-measured analog circuit by adopting a random sinusoidal signal, wherein the amplitude, phase and frequency of the random sinusoidal signal are random variables which satisfy Gaussian distribution; B. collecting output data samples of the to-be-measured analog circuit, and extracting time domain features and spectrum features of the data samples, so as to form feature components; C. respectively inputting an HMM diagnosis system with a random time sequence to each type feature component, and fusing a plurality of diagnosis results by adopting an ECOC (Error Correcting Output Codes) method, so as to realize fault diagnosis. According to the method, the random sinusoidal signal is used as test stimulation of the analog circuit, so that the frequency components of the output samples can be increased, and the superposition of a fuzzy fault group is reduced; a time sequence analysis method of the HMM is combined, so that the diagnosis precision of analog circuit fuzzy faults can be increased.

Description

technical field [0001] The invention relates to an analog circuit fault diagnosis method, in particular to an analog circuit fault diagnosis method based on a random sinusoidal signal test and a hidden Markov model based on time series analysis. Background technique [0002] With the development of electronic equipment towards intelligence and flexibility, its complexity and functionality are becoming more and more powerful. Reliability research plays an extremely important role in the design and testing of complex electronic equipment. Electronic equipment has penetrated into various fields of today's society, and analog circuits are an indispensable and important part of electronic equipment. However, due to the complex fault state of analog circuits, ambiguous fault symptoms, limited test nodes, tolerance of fault component values, and nonlinear effects of circuits, fault diagnosis of analog circuits is very difficult. [0003] According to the order of simulation in th...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01R31/316
Inventor 罗慧卢伟蹇兴亮郭海燕
Owner NANJING AGRICULTURAL UNIVERSITY
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