Crystallizer steel leakage forecasting method based on feature vectors and hierarchical clustering

Abstract

The invention belongs to the technical field of steel metallurgy continuous casting inspection, and provides a crystallizer steel leakage forecasting method based on feature vectors and hierarchical clustering. The crystallizer steel leakage forecasting method based on the feature vectors and the hierarchical clustering comprises the following steps of 1, extracting characteristic vectors of sticking steel leakage, normal working condition historical data and temperature of online measured data to establish a feature vector sample set; 2, carrying out normalization processing to the feature vector sample set, and carrying out hierarchical clustering; and 3, checking and judging whether the feature vectors extracted online belong to steel leakage clusters or not, and further identifying andforecasting steel leakage of the crystallizer. According to the crystallizer steel leakage forecasting method based on the feature vectors and the hierarchical clustering, tedious debugging and modification steps involving alarm thresholds and the like are avoided, dependence of people on a previous steel leakage forecasting method is overcome, and good robustness and mobility are achieved; through extraction of temperature characteristic, a temperature mode of the sticking steel leakage can be accurately recognized, missing reports are avoided, false alarm frequency is remarkably reduced, data calculation amount and calculation time can be greatly reduced, and real-time performance of online forecasting is ensured.

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 feature vectors and hierarchical clustering. Background technique [0002] Bonded breakout refers to the rupture of the thin primary slab shell near the meniscus during the continuous casting process. After the molten steel seeps out, it contacts with the copper plate of the crystallizer to bond. With the vibration of the mold and the downward movement of the slab, the bonding is repeatedly torn- It heals and moves downward continuously. When it moves out of the crystallizer outlet, it loses the support constraint of the copper plate, and the molten steel overflows, causing steel breakout. Steel breakout not only endangers the safety of on-site operators and seriously damages the continuous casting equipment, but also will lead to forced interruption of continuous casting production, and a ...

AI Technical Summary

Problems solved by technology

However, the limitations of this method are: 1) Randomly select some samples in all breakout sample sets for clustering, which cannot take into account the common characteristics of all samples and the individual characteristics of a single sample, that is, the coverage of sample characteristics after clustering is not comprehensive; 2) temperature The data is directly used for clustering after preprocessing, and the amount of data and calculation is large, which affects the real-time performance of online forecasting

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