A Local Dynamic Broad Kernel Stationary Subspace Analysis for Monitoring Blast Furnace Ironmaking Process

Abstract

For the actual blast furnace ironmaking process (BFIP), sophisticated dynamic, nonlinear and nonstationary characteristics make it hard to be modeled accurately with conventional monitoring methods. In this paper, local dynamic broad kernel stationary subspace analysis (Local-DBKSSA) is developed to improve the monitoring performance. Faced with complex dynamic nonlinear characteristics, a single model is considered to be unable for accurate representation. Thus, dynamic broad nonlinear features established by time shift and multi-kernel projection are adopted from more perspectives. Subsequently, the above features are integrated into stationary subspace analysis (SSA) to estimate stationary projections from time-varying data. In order to reduce the impact of large fluctuations and improve fault detection capability, a local statistic is further proposed. The effects of nonstationary characteristic on monitoring capability and the excellent performance of the local statistic are also theoretically analyzed. Finally, a case study based on actual BFIP data presents that the proposed method can discriminate between normal and sample faults more accurately and timely, and has better robustness to nonstationary perturbations under normal conditions by providing fewer false alarms.