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Leak test curve trend prediction method and system based on predictive function model

A function model and leak testing technology, applied in the field of measurement, can solve the problems of long stabilization time, limited detection efficiency, long test time, etc., and achieve the effect of simple prediction method

Active Publication Date: 2020-06-12
SHANGHAI JIAOTONG UNIV
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Problems solved by technology

The stable time of the test is long, resulting in a long test time for a single product, which greatly limits the detection efficiency

Method used

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  • Leak test curve trend prediction method and system based on predictive function model
  • Leak test curve trend prediction method and system based on predictive function model
  • Leak test curve trend prediction method and system based on predictive function model

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Embodiment Construction

[0055] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0056] Such as figure 1 As shown, a kind of leakage test curve trend prediction method based on the predictive function model provided by the present invention includes:

[0057] Step 1, classify according to the leak test curve obtained from the leak test historical data;

[0058] Step 2, estimate the description function model for different types of leak test curves;

[0059] Step 3, in the application link, classify a leak test to be predicted by the classification method of machine learning;

[0...

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Abstract

The invention provides a leakage test curve trend prediction method and system based on estimation function model. The leakage test curve trend prediction method based on estimation function model comprises the steps of classifying leakage test curves obtained by leakage historical data; performing estimation of an estimation function model on different types of leakage test curves; classifying to-be-predicted leakage tests through machine learning; according to a classification result, importing a to-be-predicted leakage test into the corresponding estimation function model to estimate modelparameters; and carrying out leakage curve trend prediction by utilizing the model of the obtained parameters. According to the method, historical data are effectively utilized, machine learning is ingeniously combined with a method capable of being used for classification, then a description function of a leakage test curve is obtained in a parameter regression mode, and therefore trend prediction of the leakage test is achieved, the time of the leakage test is shortened, and the leakage detection efficiency of containing cavity type products is improved.

Description

technical field [0001] The present invention relates to the field of measurement technology, in particular to a leak test curve trend prediction method and system based on a prediction function model. Background technique [0002] At present, the differential mass flow leakage test method used in the cavity tightness test needs to go through a series of processes including inflation, stabilization, and finally giving the test results. [0003] The patent with the publication number of CN203893991U discloses a sealing test device, comprising: an electric control box, an upper box body arranged on the electric control box, a barometer, a water tank, an air inlet pipe, and an outlet pipe arranged on the box body. Gas pipe and differential transmitter; the air pressure gauge and differential transmitter are electrically connected to the electric control box, one end of the air inlet and outlet pipes is externally connected to the product to be tested, and the other end of the ai...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/20
Inventor 罗磊
Owner SHANGHAI JIAOTONG UNIV
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