Blast furnace (BF) ironmaking is a highly complicated process with multi-variable nonlinear coupling and multi-mode characteristics. In this article, a developed kernel function partial derivative contribution (KF-PDC) is proposed for abnormality location, which makes up for the deficiency of linear multivariate statistical process monitoring (MSPM) and de-redundancies the variable candidate set of causality analysis. Then, to eliminate the influence of multi-mode, an online interval adaptive causation entropy (IACE) is established to analyse the cause–effect relationships of candidate abnormal variables, which contributes to distinguishing the direct and indirect causality, and the Haar wavelet based on the sliding window (HWSW) is constructed for the segmentation of different modes online. Finally, a case study using actual industrial BF ironmaking data illustrates that the monitoring method can better capture the abnormal furnace conditions and effectively obtain the root cause and propagation path.
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