1.西安建筑科技大学 建筑设备科学与工程学院;2.西安建筑科技大学 信息与控制工程学院;3.中冶京诚数科公司;4.西安建筑科技大学 信息与控制工程学院
国家自然科学基金面上项目（52278125）
1.Xi'2.'3.an University of Architecture and Technology, School of Architectural Equipment Science and Engineering;4.an University of Architecture and Technology, School of Information and Control Engineering;5.MCC Jingcheng Digital Technology Co., Ltd;6.Xi&7.amp;8.#39;9.&10.Xi'an University of Architecture and Technology
National Natural Science Foundation of China (NSFC) General Program (52278125)
在热轧无缝钢管CPE顶管生产过程中,顶管机的能耗管理面临挑战,主要源于设备和工艺等多源异构数据的特征信息提取困难,进而影响电耗预测的准确性。针对高维、非线性和时变特征的电耗预测问题,本文提出了一种融合多源异构数据的预测模型。该模型首先结合卷积神经网络(CNN)进行空间特征提取。接着,采用Transformer编码器对时序特征建模。最后,通过交叉注意力机制(CA)实现数据融合。通过对实际生产中的历史数据进行预处理、特征提取与融合,建立了电耗回归预测模型。实验结果表明,所提出的模型在预测精度上显著优于传统方法,决定系数(R2)超过98%。该研究为CPE顶管机能耗管理提供了有效的技术支持,具有较高的工程应用价值。
In the hot rolling process of seamless steel pipes, the energy consumption management of the CPE push bench mill faces challenges, primarily due to the difficulty in extracting feature information from multi-source heterogeneous data such as equipment and process parameters, which in turn affects the accuracy of power consumption prediction. To address the issue of high-dimensional, nonlinear, and time-varying features in power consumption prediction, this paper proposes a prediction model that integrates multi-source heterogeneous data. The model first combines Convolutional Neural Networks (CNN) for spatial feature extraction. Then, a Transformer encoder is used for modeling temporal features. Finally, the data fusion is achieved through a Cross-Attention mechanism (CA). By preprocessing, feature extraction, and fusion of historical data from actual production, a power consumption regression prediction model is established. Experimental results show that the proposed model significantly outperforms traditional methods, with the coefficient of determination (R2) exceeding 98%. This research provides effective technical support for the energy consumption management of the CPE push bench mill and has high engineering application value.
