The edge surface warping defect seriously affect the surface quality of strips. In this paper, a technology for diagnosis of warping defects in hot-rolled strip based on data-driven methods is studied. Based on the mechanism analysis of the warping defects, the process parameters affecting the warping defects were sorted out and used as the original input parameters of the defect diagnosis model. Firstly, a diagnostic model that combines the deep belief network and contribution plots of each dimensionality reduction layer is proposed. The deep belief network that integrates each dimensionality reduction layer can predict product defects more accurately and stably than the traditional deep belief network. Meanwhile, on the basis of the pre-judgment model, the method of contribution plot is further introduced to trace the defects, and the comprehensive diagnosis function of model pre-judgment and traceability is realized. Finally, collected the production data from a hot rolling production line for a period of time. Tested the model and predicted a hit rate of 85%. The main influencing factors of edge surface warping defects were determined that the rate of defect decrease with the increase of furnace temperature. When the heating temperature of the second stage of the heating furnace is higher than 1160°C, the incidence of defects is significantly reduced. Defect rate is relatively low within 240min of total furnace time. With the first and third pass phosphorus removal equipment turned on, the incidence of defects was relatively low.

边缘表面翘曲缺陷严重影响带材的表面质量。本文研究了一种基于数据驱动方法的热轧带材翘曲缺陷诊断技术。在对翘曲缺陷进行机理分析的基础上，筛选出影响翘曲缺陷的工艺参数，并将其作为缺陷诊断模型的原始输入参数。首先，提出了一种结合了深度信念网络和每个降维层贡献图的诊断模型。集成了每个降维层的深度置信网络可以比传统的深度置信网络更准确，更稳定地预测产品缺陷。同时，在预判断模型的基础上，进一步引入了贡献图法来跟踪缺陷，实现了模型预判和溯源的综合诊断功能。最后，在一段时间内从热轧生产线收集了生产数据。测试了模型并预测了85％的命中率。确定了边缘翘曲缺陷的主要影响因素，即随着炉温的升高，缺陷率降低。当加热炉的第二阶段的加热温度高于1160℃时，缺陷的发生率显着降低。在整个熔炉时间的240分钟内，缺陷率相对较低。在第一和第三遍除磷设备开启的情况下，缺陷的发生率相对较低。在一段时间内从热轧生产线收集了生产数据。测试了模型并预测了85％的命中率。确定了边缘翘曲缺陷的主要影响因素，即随着炉温的升高，缺陷率降低。当加热炉的第二阶段的加热温度高于1160℃时，缺陷的发生率显着降低。在整个熔炉时间的240分钟内，缺陷率相对较低。在第一和第三遍除磷设备开启的情况下，缺陷的发生率相对较低。在一段时间内从热轧生产线收集了生产数据。测试了模型并预测了85％的命中率。确定了边缘翘曲缺陷的主要影响因素，即随着炉温的升高，缺陷率降低。当加热炉的第二阶段的加热温度高于1160℃时，缺陷的发生率显着降低。在整个熔炉时间的240分钟内，缺陷率相对较低。在第一和第三遍除磷设备开启的情况下，缺陷的发生率相对较低。当加热炉的第二阶段的加热温度高于1160℃时，缺陷的发生率显着降低。在整个熔炉时间的240分钟内，缺陷率相对较低。在第一和第三遍除磷设备开启的情况下，缺陷的发生率相对较低。当加热炉的第二阶段的加热温度高于1160℃时，缺陷的发生率显着降低。在整个熔炉时间的240分钟内，缺陷率相对较低。在第一和第三遍除磷设备开启的情况下，缺陷的发生率相对较低。