Data-Driven Monitoring and Diagnosing of Abnormal Furnace Conditions in Blast Furnace Ironmaking: An Integrated PCA-ICA Method

Abstract

Principal component analysis (PCA) and independent component analysis (ICA) have been widely used for process monitoring in process industry. Since the operation data of blast furnace (BF) ironmaking process contain both non-Gaussian distribution data and Gaussian distribution data, the above single PCA or ICA method hardly describes the data distribution information of the BF process completely, which makes the monitoring and diagnosis of abnormal working-conditions only with a single method prone to false positives and false negatives. In this article, a novel integrated PCA-ICA method is proposed for monitoring and diagnosing the abnormal furnace conditions in BF ironmaking by comprehensively considering and combining the characteristics of PCA and ICA. First, the process monitoring models of PCA and ICA are, respectively, established using the actual industrial BF data, while both them are using T 2 and squared prediction error statistics to monitor whether the process is abnormal. Based on this, in order to fully reveal the internal structure of actual BF ironmaking data, an integrated PCA-ICA strategy and algorithm is proposed for comprehensively monitoring and diagnosing the abnormal furnace conditions. The corresponding unified contribution charts indices and control limits for fault identification were also presented. Finally, data experiments using actual industrial BF data show that the proposed method can obtain good results in both monitoring and diagnosing the abnormal furnace conditions of BF ironmaking.