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- W4386631100 abstract "In order to address the problem of abnormal state identification in blast furnace ironmaking process, an improved Mann-Kendall (MK) method is proposed for online abnormal state detection. Firstly, the industrial process of blast furnace ironmaking is analyzed. The silicon content in hot metal is taken as the process vanable, and the time series sampling data of silicon content are obtained by online monitoring. Then, based on the memory and forgetting strategies, the original MK method suitable for single state change point detection is improved to detect multiple abnormal state change points. That is, the change point Satisfying the normal distribution constraints in the online time series are regarded as the abnormal state change point, and the abnormal state change point is memorized as the new starting point of the time series, and all the sampling points before the abnormal state change points are intentionally forgotten. With the continuous extension of silicon content sampling sequence, the multiple normal/abnormal state change points can be detected in real time. Finally, the proposed method is verified by an industrial example, which shows that the proposed method can realize online detection of multiple abnormal states in blast furnace ironmaking." @default.
- W4386631100 created "2023-09-13" @default.
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- W4386631100 date "2023-07-14" @default.
- W4386631100 modified "2023-10-18" @default.
- W4386631100 title "Online detection method for abnormal states of blast furnace system based on memory and forgetting strategies" @default.
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- W4386631100 doi "https://doi.org/10.1109/cfasta57821.2023.10243275" @default.
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