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.