A deep learning framework for predicting slab transverse crack using multivariate LSTM-FCN in continuous casting

Abstract

Accurate and timely predictions of transverse cracks in slabs are crucial for ensuring efficient and high-quality production in continuous casting. However, the accumulation of substantial amounts of complex, multi-dimensional time series data during the casting process considerably affects the modeling performance. In order to increase the prediction accuracy, this paper introduces a novel prediction framework for slab transverse cracks (PF-STC), which comprises three stages: data preparation, data processing, and model training. First, a synchronized scheme is developed for real-time data tracking to achieve one-to-one matching between the process data in the data preparation phase. Second, the data preprocessing stage tackles the multidimensionality of time series data through normalization and mean aggregation downsampling. Finally, in the model training phase, a multivariate long short-term memory-fully convolutional network (MLSTM-FCN) algorithm is utilized to improve the predictive performance. Experimental results on a real dataset from a steel manufacturing company demonstrate that the PF-STC framework outperforms recent state-of-the-art methods, reducing the number of parameters by 49.97% and training time by 33.83%. The PF-STC achieves an AUC score of 0.9435, with accuracy, precision, recall, and F1 scores of 0.9163, 0.8657, 0.8286, and 0.8467, respectively. The proposed PF-STC is poised to provide support for online transverse crack monitoring, with the application demands of monitoring production processes for the steel industry.