Industrial Data-driven Intelligent Forecast for Chatter of Cold Rolling of Thin Strip with LSTM Recurrent Neural Network

The chatter in the cold tandem rolling process is an old problem here, which is more prominent with the demand of thinner specifications, higher strength and higher rolling velocity. It is hard to rely too much on experiences to suppress chatter timely and effectively because of the complex and changeful induced mechanism, therefore the intelligent forecasting of chatter in cold rolling based on historical vibration big data is an important application scenario for intelligent rolling. Considering the multi-source, polymorphism and strong coupling characteristics of industrial data for cold rolling chatter, the sample space of chatter prediction model is built with data preprocess technique of multi-source data acquisition and interaction, moment matching, frequency coordination and data dimensionality reduction. The intelligent forecast model for the chatter is formulated based on LSTM recurrent neural network. Using the information of historical data of rolling product specifications, roll condition, rolling process parameters and vibration of mill, the vibration energy of fifth stand the most typical and frequently vibrating is predicted, and the influence of the number of time step on the prediction effect is analyzed to obtain the optimal number of prediction step. The trend of the results on the prediction of model is basically consistent with the trend of the actual data, the mean square errors of the training data set and the test data set are small. Then, the model is applied to the actual process data which are not involved in the training and testing process, and it is concluded that the LSTM model can effectively predict mill chatter in advance according to the alarm threshold. The results of research demonstrate that the deep learning and data mining based on multi-source historical vibration data can realize the intelligent forecast of chatter instability in the cold tandem rolling process, which not only play an important role in actual production, but have a promising future for promoting the intelligent of cold rolling process. © 2020 Journal of Mechanical Engineering.