Abnormal Condition Recognition of Blast Furnace Ironmaking Process Based on Time Series—Image Jointly Driven Deep Neural Network

Ensuring the accurate recognition of blast furnace (BF) conditions is crucial for maintaining the stability of blast furnace ironmaking process (BFIP). However, many research frequently overlooks the comprehensive utilization of the closely correlated multisource heterogeneous data (MHD) of BFIP, such as time series and images, resulting in an incomplete understanding of BF conditions and an unsatisfactory accuracy of condition recognition. Therefore, this article proposes a novel BF condition recognition method by incorporating both time series and images of BFIP. First, a data alignment model using the Gaussian functions is proposed to align time series with images. Then, an innovative Time Series—Image jointly driven deep neural Network (TSIN) is established to extract and fuse features from MHDs. Subsequently, a dual-layer residual-connected module is designed to capture the correlations between MHDs. Furthermore, a stability evaluation metric is devised to quantify the stability of TSIN during the model training process. Industrial experiments demonstrate that the proposed method, with an average recognition accuracy of 96.99%, could effectively recognize abnormal conditions, such as channeling, hanging, slipping, and collapsing, providing practical guidance for on-site workers to monitor and regulate the BFIP.