基于改进降噪自编码器半监督学习模型的热轧带钢水梁印识别算法

针对水梁印识别困难且工作量大问题，提出一种基于改进降噪自编码器半监督学习模型的热轧带钢水梁印识别算法。该算法在降噪自编码器（Denoising auto-encoder, DAE）的基础上对编码层的每一层添加随机噪声，在隐藏层后添加分类层，并对数据添加伪标签，在解码的同时进行分类训练，使得DAE具有半监督学习能力。通过提取热轧带钢粗轧出口温度数据中的温差特征，用相应特征对模型进行训练。实验结果表明，算法能够准确识别出带钢的水梁印，在模型精确度上，与主流分类识别模型对比，提出的模型在带标签样本数量较小时，分类精度相比其他模型高5.0%～10.0%；在带标签样本数量较大时，提出的模型分类精度达到93.8%，现场能够根据模型的识别结果提高生产效率。 

Recognition algorithm of hot-rolled strip steel water beam mark based on a semi-supervised learning model of an improved denoising autoencoder

The water beam mark is a common problem in slab heating, which causes quality defects on strip steel. In hot strip rolling, the heating quality of the slab considerably influences the rolling stability and quality of the finished strip. The water beam mark caused by the heating process and equipment is a common defect in the slab heating. A slab water beam imprint has a great influence on the control precision of the rolling force and thickness of the finished strip. Presently, recognizing the water beam mark is difficult and the workload in the industry is heavy. To solve these problems, this study proposed a recognition algorithm of a hot-rolled strip steel water beam mark based on a semisupervised learning model of an improved denoising autoencoder (DAE). Based on the DAE, random noise was added to each layer of the coding layer, a classification layer was added after a hidden layer, and fake labels were added to the training data. Decoding and classification training are conducted simultaneously. These methods result in the model becoming semisupervised. In this study, we extract the temperature difference of the strip temperature data at the outlet of the roughing mill and use it to train the model. Experimental results showed that the algorithm can accurately recognize the water beam mark of strip steel. The classification accuracy of the proposed model is 5.0%–10.0% higher than other mainstream models when the number of tag proportions is small. When the number of tag proportions is large, the accuracy of the proposed model reaches up to 93.8%. According to the result, the production efficiency can be improved using this model.