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First Name:
Siwei
Last Name:
Lou
Datasets & Competitions
In view of blast furnace ironmaking process (BFIP), the co-existence of dynamics and nonstationarity causes it extremely difficult to build an effective fault detection model for securing safety and reliability. First, to explore the hybrid properties in the dynamic nonstationary system more explicitly, we established an inferential observation decomposition strategy by combining independent nonstationary, static, and dynamic components.
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74 Views
In this paper, a novel time-constrained global and local nonlinear analytic stationary subspace analysis (Tc-GLNASSA) is proposed to enhance blast furnace ironmaking process (BFIP) monitoring. Although existing analytic stationary subspace analysis method has been available for deriving process consistent relationships. However, the presence of complex nonlinear, periodic nonstationary and time-varying smelting conditions renders the satisfactory estimation of stationary projections unattainable.
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170 Views
Blast furnace iron-making process (BFIP) is one of the most critical procedures in the iron and steel industry, in which timely detection and accurate classification of faults have always been of core focus. Nevertheless, due to the coupling effects of complex nonlinear and nonstationary characteristics hidden among the data, the consistent underlying information in the process cannot be accurately mined, hindering the establishment of the BFIP fault diagnosis model.
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108 Views
In blast furnace iron-making process (BFIP), there is a significant push to maintain a stable iron-making process and ensure process at maximum efficiency. While some control systems can compensate for multiple types of disturbances when faults occur, some significant process faults often require precise human intervention to avoid safety hazards. Therefore, it is crucial to develop an efficient and stable diagnostic system to efficiently identify these faults so that operators can deal with them quickly.
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27 Views