A novel industrial process situation awareness model based on multi‐time scale dynamic feature fusion with applications to float glass manufacturing

Abstract

AbstractModern industrial manufacturing processes often focus on the changes of performance indicators and the evolution of operation conditions. However, the dynamics of variables are vague within processes and even more difficult to be characterised across different processes. The time‐series dynamic characteristics of the different scales among variables, processes, and production cycles develop and transfer with changes in parameters, equipment, and processes. It is difficult to accurately show the quality index and operating condition trend at the same time. To solve these problems, a situation awareness (SA) framework integrating multi‐time scale dynamic features for manufacturing processes is proposed. First, data are condensed and reconstructed through the denoising long short term memory autoencoder to reveal the time series dynamic characteristics to get neighbourhood features which contain features among neighbourhood samples. Second, the neighbourhood features divided into window blocks are fused into the stage features by statistical analysis. Finally, a multi‐scale isometric convolution network is designed to extract the local and global features, which can effectively show the development of dynamic features on a long time scale, and profoundly describe the influence of full cycle features on variables and operating conditions. The proposed model is verified on the real data set of a float glass manufacturing process, and the SA model can well predict the future trend of long time series.