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

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...

AI Technical Summary

Problems solved by technology

The method based on hierarchical clustering gets rid of the limitations of artificially setting and adjusting forecast parameters, but it is difficult to meet the real-time and online application requirements of breakout forecast, so the model is less practical

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