Multi time scale inception-time network for soft sensor of blast furnace ironmaking process

Time series(TS) forecasting has been widely applied in many fields and industrial soft sensor is one of them. Most time series modeling methods require that all inputs are sampled at equal intervals. However, in industry, the process variables are often sampled on different time scales with varying intervals. To address this problem, in this paper, we designed a framework using deep learning with time representation techniques to model the long temporal industrial data with multiple sampling frequency. Data are aggregated into different time scale by time representation and the network extracts the information on both temporal and spatial dimensions simultaneously using the bottleneck layer and one-dimensional filter. The proposed model has a significant improvement compared with other methods and has been deployed in the factory and updated every month. (C) 2022 Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a framework using deep learning and time representation techniques to model long temporal industrial data with multiple sampling frequency. By aggregating the data into different time scales and extracting information using a bottleneck layer and one-dimensional filter, the proposed model achieves significant improvement and has been deployed in the factory.