基于动态时间弯曲和k近邻分类预测铸坯纵裂纹

纵裂纹是典型的铸坯表面缺陷,严重影响过程顺行和连铸坯质量。铸坯表面纵裂纹的识别和预测对于铸坯质量的提升具有重要意义。针对纵裂纹形成与扩展过程中,结晶器热电偶温度在时间序列上的动态演化和一维传播特征,捕获和提取了纵裂纹和正常工况下热电偶时序温度的典型变化趋势;运用动态时间弯曲(Dynamic Time Warping,DTW)方法度量不同工况下时序温度的相似性和差异性,结合k近邻(k-Nearest Neighbor,kNN)分类算法,建立了针对连铸坯纵裂的在线识别和分类模型。结果表明,建立的模型对纵裂纹和正常工况样本具有较高的识别准确率。提出的基于温度时序特征动态时间弯曲及kNN方法的纵裂预测模型,为铸坯纵裂的实时检测和准确预报提供了可靠途径。

Predicting longitudinal crack of slab based on dynamic time warping and k-Nearest Neighbor

Longitudinal cracks are typical surface defects of slabs, which seriously affect the quality of casting slabs and process progression. The identification and prediction of longitudinal cracks is of great significance to improve the quality of the cast slabs. According to the dynamic evolution and one-dimensional propagation characteristics of the mould thermocouple temperature in time series during the formation and propagation of longitudinal cracks, the typical variation trends of the thermocouple temperature in time series under longitudinal cracks and normal conditions were captured and extracted. Dynamic Time Warping (DTW) method is used to measure the similarity and difference of time series temperature under different working conditions, and k-Nearest Neighbor (kNN) classification algorithm is used to establish online identification and classification model for longitudinal crack of continuous casting billet.The results show that the model has a high accuracy rate for the identification of longitudinal crack and normal condition samples, which provides a reliable way for real-time detection and accurate prediction of slab longitudinal cracks.