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Test stimulus and optimizing method based on response aliasing measurement and genetic algorithm

A genetic algorithm and test incentive technology, applied in genetic rules, calculations, genetic models, etc., can solve problems such as blurred boundaries, slow speed of test incentive optimization methods, and low early fault detection rate, so as to speed up the optimization speed and improve the early simulation Detection rate, the effect of improving the early fault detection rate

Active Publication Date: 2017-12-15
HARBIN INST OF TECH
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem that in the existing analog circuit, due to the existence of device tolerance, the boundary between the fault state and the normal state of the device is blurred, resulting in a low early fault detection rate, and the slow speed of the analog circuit test excitation optimization method, and proposes A Test Stimulus Optimization Method Based on Response Aliasing Measurement and Genetic Algorithm

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  • Test stimulus and optimizing method based on response aliasing measurement and genetic algorithm
  • Test stimulus and optimizing method based on response aliasing measurement and genetic algorithm
  • Test stimulus and optimizing method based on response aliasing measurement and genetic algorithm

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specific Embodiment approach 1

[0039] Specific implementation mode one: a method for optimizing test incentives based on response aliasing measurement and genetic algorithm comprises the following steps:

[0040] Step 1: Obtain P frequency points to be optimized at equal intervals within the entire frequency band, and obtain all characteristic information of the circuit M times of normal operation and all the fault states caused by the faulty component H at each frequency point. Feature information, that is, to obtain M normal samples and M fault samples; Step 2: Use genetic algorithm to binary code P frequency points and initialize parameters;

[0041] The parameter initialization includes: the population size selected from the P frequency points is NIND frequency points, the genetic algebra is MAXGEN, the crossover probability p1, and the mutation probability p2;

[0042] Step 3: The genetic algorithm uses the response aliasing metric function as the fitness function, and calculates the fitness function v...

specific Embodiment approach 2

[0045] Embodiment 2: This embodiment differs from Embodiment 1 in that: the interval frequency in the step 1 is 1-5 Hz.

[0046] Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0047] Embodiment 3: The difference between this embodiment and Embodiment 1 or 2 is that all the feature information in Step 1 is the voltage value and phase value corresponding to each frequency point.

[0048] Other steps and parameters are the same as those in Embodiment 1 or Embodiment 2.

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Abstract

The invention relates to a test stimulus and optimizing method based on a response aliasing measurement and a genetic algorithm, and aims at solving the problems in an existing simulation circuit that since the boundary of a device failure state and a normal state is fuzzy due to the existence of device tolerance, early failure detection is poor, and the test stimulus optimizing speed is low. The test stimulus and optimizing method comprises the steps of 1, obtaining M normal samples and M failure samples; 2, adopting the genetic algorithm to conduct binary encoding on P frequency points and initialize parameters; 3, adopting a response aliasing measurement function in the genetic algorithm as a fitness function and calculating fitness function values of NIND frequency points; 4, obtaining a binary gene of a test stimulus of which the response aliasing measurement function value is the smallest and obtaining a corresponding optimal test stimulus after encoding. The test stimulus and optimizing method based on the response aliasing measurement and the genetic algorithm is applied to the field of simulation circuit failure diagnosis.

Description

technical field [0001] The invention relates to a test excitation optimization method based on response aliasing measure and genetic algorithm. Background technique [0002] With the development of national defense science and technology in our country, electronic systems are widely used in missile control, communication, target detection, identification of friend or foe and other fields, so the reliability of electronic systems determines the performance of weapons and equipment. Although the proportion of analog circuits in electronic systems is about 20%, most of the faults in electronic systems come from analog circuits. One of the main factors affecting the performance of analog circuits is faults caused by parameter drift of components in the circuit. The current field of fault diagnosis The types of faults involved are mainly soft faults and hard faults caused by large device deviations. Since these two types of faults have a greater impact on circuit performance and ...

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Application Information

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IPC IPC(8): G06F17/50G06N3/12
CPCG06F30/367G06N3/126
Inventor 俞洋姜月明杨智明彭喜元季雪松
Owner HARBIN INST OF TECH
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