Crystallizer bleed-out forecasting method based on feature vector and logistic regression model

A feature vector and regression model technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of difficult to meet the real-time and online application requirements of breakout forecast, and low model practicability, so as to improve the accuracy of forecast. The effect of reducing the false alarm rate

Pending Publication Date: 2021-10-15
DALIAN UNIV OF TECH
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Problems solved by technology

The method based on hierarchical clustering gets rid of the limitations of artificially setting and adjusting forecast parameters,

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  • Crystallizer bleed-out forecasting method based on feature vector and logistic regression model
  • Crystallizer bleed-out forecasting method based on feature vector and logistic regression model
  • Crystallizer bleed-out forecasting method based on feature vector and logistic regression model

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[0050] Next, in connection with the accompanying drawings, the present invention is further elaborated in conjunction with the accompanying drawings.

[0051] like figure 1 The flow chart shown is shown in the crystallizer leakage steel forecasting method. First, extract the visualization characteristics of the crystallizer copper temperature rate and the pretreatment of it, the structure is obtained, and the six-dimensional special vector; secondly, the logic regression model is adjusted and trained; finally, through the training good logic regression model Classify and drain steel forecasts for feature vectors.

[0052] First step, crystallizer copper plate temperature rate visualization and abnormal regional feature extraction

[0053] (1) such as figure 2 The displayed copper plate and its thermocouple are shown in the crystallizer. The crystallizer is combined by four copper plates, with a total height of 900 mm, and the effective height of 800 mm is 800 mm. In the crystalliz...

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Abstract

The invention provides a crystallizer bleed-out forecasting method based on a feature vector and a logistic regression model, and belongs to the technical field of ferrous metallurgy continuous casting detection. According to the crystallizer bleed-out forecasting method, visual feature vectors are extracted from a crystallizer copper plate temperature rate abnormal region, and the feature vectors are classified by using a logistic regression model, so that crystallizer bleed-out is detected and forecasted. According to the method, the characteristic vectors containing static and dynamic characteristics of the bonding area are constructed through the visual thermogram of the temperature rate of the crystallizer copper plate, and the characteristic vectors are classified through the logistic regression model, so that the bleed-out of the crystallizer is detected and forecasted. The crystallizer bleed-out is detected and forecasted in real time based on the logistic regression model, the false alarm rate can be obviously reduced on the premise of ensuring that all bleed-out is reported, and then the forecasting accuracy is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of iron and steel metallurgical continuous casting detection, and relates to a mold breakout prediction method based on eigenvectors and logistic regression models. Background technique [0002] In the continuous casting process, the non-uniformly solidified primary billet shell in the crystallizer cannot bear the dual effects of hydrostatic pressure and casting force, and is prone to fracture at weak points to form breakouts. Breakout is a major safety accident in continuous casting production, which not only endangers personal safety, damages equipment, and even causes production to be interrupted, affecting the output and product quality of the casting machine. With the continuous development and progress of continuous casting technology, the probability of steel breakout can be effectively reduced by standardizing operation and maintaining the equipment in good operating condition. Breakout is closely r...

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Application Information

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IPC IPC(8): G06K9/32G06K9/34G06K9/62B22D11/16
CPCB22D11/16G06F18/214G06F18/2415
Inventor 王旭东王砚宇段海洋姚曼
Owner DALIAN UNIV OF TECH
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