Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction mode...Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.展开更多
以风电机组轴承为研究对象,利用SCADA(Supervisory Control And Data Acquisition)监测参数,应用最小二乘曲面拟合算法,建立轴承温度健康状态劣化趋势模型。改进并应用EEMD(Ensemble Empirical Mode Decomposition)方法,分解具有非平稳...以风电机组轴承为研究对象,利用SCADA(Supervisory Control And Data Acquisition)监测参数,应用最小二乘曲面拟合算法,建立轴承温度健康状态劣化趋势模型。改进并应用EEMD(Ensemble Empirical Mode Decomposition)方法,分解具有非平稳性特性的轴承劣化趋势为一系列相对平稳的分量,利用时间序列神经网络分别对各分量单独预测,叠加所有分量的预测值作为最终的预测结果。经过仿真测试,该方法能够以更高的精度预测风电机组轴承健康状态劣化趋势。展开更多
基金Project(2023JH26-10100002)supported by the Liaoning Science and Technology Major Project,ChinaProjects(U21A20117,52074085)supported by the National Natural Science Foundation of China+1 种基金Project(2022JH2/101300008)supported by the Liaoning Applied Basic Research Program Project,ChinaProject(22567612H)supported by the Hebei Provincial Key Laboratory Performance Subsidy Project,China。
文摘Mill vibration is a common problem in rolling production,which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases.The existing vibration prediction models do not consider the features contained in the data,resulting in limited improvement of model accuracy.To address these challenges,this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model(MDMMVPM)based on the deep fusion of multi-level networks.In the model,the long-term and short-term modal features of multi-dimensional data are considered,and the appropriate prediction algorithms are selected for different data features.Based on the established prediction model,the effects of tension and rolling force on mill vibration are analyzed.Taking the 5th stand of a cold mill in a steel mill as the research object,the innovative model is applied to predict the mill vibration for the first time.The experimental results show that the correlation coefficient(R^(2))of the model proposed in this paper is 92.5%,and the root-mean-square error(RMSE)is 0.0011,which significantly improves the modeling accuracy compared with the existing models.The proposed model is also suitable for the hot rolling process,which provides a new method for the prediction of strip rolling vibration.
文摘以风电机组轴承为研究对象,利用SCADA(Supervisory Control And Data Acquisition)监测参数,应用最小二乘曲面拟合算法,建立轴承温度健康状态劣化趋势模型。改进并应用EEMD(Ensemble Empirical Mode Decomposition)方法,分解具有非平稳性特性的轴承劣化趋势为一系列相对平稳的分量,利用时间序列神经网络分别对各分量单独预测,叠加所有分量的预测值作为最终的预测结果。经过仿真测试,该方法能够以更高的精度预测风电机组轴承健康状态劣化趋势。
