The Time–Frequency Analysis and Prediction of Mold Level Fluctuations in the Continuous Casting Process

Mold level fluctuation significantly affects the stability and quality of the slab during the continuous casting process. However, traditional mechanism models are insufficient for providing accurate time-series predictions under complex and multivariable operating conditions. Additionally, the dynamic interdependencies between process variables and transient abnormal fluctuation events have been largely overlooked in existing studies. To address these limitations, we propose an integrated time–frequency characterization and prediction framework that combines multi-domain feature extraction with a long-sequence Informer model. First, the preprocessing pipeline transforms heterogeneous sensor data into standardized time series through normalization and standardization, thereby establishing a robust foundation for subsequent feature extraction and predictive modeling. Second, the time–domain and frequency–domain feature extraction methods are integrated to capture essential patterns in casting signals with improved resolution and interpretability. Third, the fusion features are embedded into a time-series prediction model, which performs robust forecasting of mold level behavior and enhances the identification of root causes behind fluctuation anomalies. Compared with conventional LSTM and Transformer models, the proposed framework achieves over 90% reduction in prediction error and provides interpretable insights into the correlations between casting parameters and mold level variations. Finally, real industrial experimental results demonstrate the performance of the proposed framework in enhancing prediction reliability and providing insight into fluctuations with scalable implementation.