Fault Diagnosis of Blast Furnace Iron-Making Process With a Novel Deep Stationary Kernel Learning Support Vector Machine Approach

In the blast furnace iron-making process (BFIP), there still has been a significant push to maintain a stable process and ensure maximum efficiency. Although some control systems can compensate for multiple types of disturbances, some significant process faults always require precise human intervention to avoid safety hazards. Therefore, it is crucial to develop an efficient and stable diagnosis system to identify these faults quickly. This article focuses on a novel approach called deep stationary kernel learning support vector machine (DSKL-SVM) for BFIP fault diagnosis. To eliminate the impact of nonstationary property on modeling, analytic stationary subspace analysis (ASSA) is adopted to estimate consistent underlying features. Then, design a multilayer stacked deep kernel network to explore deep nonlinear information further. A DSKL-SVM classifier and the corresponding two-tier model optimization algorithm are constructed to isolate different data types. At last, a series of case studies based on actual BFIP present the effectiveness of DSKL-SVM. The results demonstrate that the proposed method has an outstanding effect on fault diagnosis, and it is verified that the performances of stationary projection (SP), deep stationary nonlinear (DSN) construction, and online computation times are superior to other methods.