| description abstract | Abstract. Segment is a key component of a continuous casting machine, which frequently experiences faults. The straightening force is an important indicator for the working state of the continuous casting machine, effectively reflecting the segment fault. To provide data support for the identification of segment faults quickly and accurately, an approach for straightening force prediction based on the model-agnostic meta-learning-Long Short-Term Memory (MAML-LSTM) is proposed. First, the characteristics of the straightening force for the segment are analyzed, and the corresponding straightening force time-series data are preprocessed using wavelet transform adaptive threshold for reducing the interference of external factors. Second, through analysis of influencing factors for straightening force, the straightening force dataset is constructed. After that, to address the issue of a poor sample of segment fault, the MAML-LSTM model for straightening force prediction is built, where the straightening force dataset is employed for meta-training and meta-testing. Finally, the results of the experiment verify the effectiveness and feasibility of the proposed approach, which can effectively improve the accuracy of straightening force prediction. It can provide valuable data support for segment fault identification in continuous casting. | |
