Longitudinal cracks are the typical surface defects of continuous casting slabs, resulting in additional processing or even casting interruption. Monitoring longitudinal crack defects is of great significance in stabilizing and improving the slab quality. Herein, a monitoring model is developed to recognize the longitudinal crack defect of continuous casting slabs using principal component analysis (PCA) and support vector machine (SVM). First, the typical characteristics of the temperature patterns corresponding to the longitudinal crack defect are extracted, including the normal casting temperature with small and large fluctuations, as well as the longitudinal crack temperature. Then, PCA is used to reduce the dimension of these characteristics for removing the redundancy and reducing the computational burden. Subsequently, an SVM is applied to identify normal and longitudinal crack temperature patterns for training the monitoring model PCA–SVM. The monitoring performance is verified by the test data, in which the training accuracy and test accuracy are 100% and 96%, respectively. It is worth mentioning that the model can successfully predict all the real longitudinal crack defects, showing excellent detection performance. The established model is expected to provide a theoretical basis and a reliable way for online monitoring of slab surface cracks.

中文翻译：

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基于主成分分析和支持向量机的板坯连铸纵向裂纹检测方法

纵向裂纹是连铸板坯的典型表面缺陷，导致附加加工甚至连铸中断。监测纵向裂纹缺陷对于稳定和提高板坯质量具有重要意义。在此，开发了一种使用主成分分析 (PCA) 和支持向量机 (SVM) 识别连铸板坯纵向裂纹缺陷的监测模型。首先，提取纵向裂纹缺陷对应的温度模式的典型特征，包括波动较小和较大的正常铸造温度，以及纵向裂纹温度。然后，PCA 用于降低这些特征的维数，以去除冗余并减少计算负担。随后，应用 SVM 来识别正常和纵向裂纹温度模式，以训练监控模型 PCA-SVM。通过测试数据验证了监控性能，其中训练准确率和测试准确率分别为100%和96%。值得一提的是，该模型能够成功预测所有真实的纵向裂纹缺陷，表现出优异的检测性能。建立的模型有望为板坯表面裂纹的在线监测提供理论依据和可靠途径。值得一提的是，该模型能够成功预测所有真实的纵向裂纹缺陷，表现出优异的检测性能。建立的模型有望为板坯表面裂纹的在线监测提供理论依据和可靠途径。值得一提的是，该模型能够成功预测所有真实的纵向裂纹缺陷，表现出优异的检测性能。建立的模型有望为板坯表面裂纹的在线监测提供理论依据和可靠途径。