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- W4385688209 abstract "The quality of the acquisition data in the energy system of steel industry is the basis of prediction analysis and scheduling operation. Facing with its multi-dimensional and high-noise characteristics, in this study, an anomaly detection method based on a varying-scale hypercube accelerated density based spatial clustering for applications with noise (VHCA-DBSCAN) is proposed. An HCA-DBSCAN model based on the Gaussian probability density estimation is firstly established, in which the searching radius is adaptively calculated to identify the suspected outliers efficiently. Then, a traversal search strategy for hypercube segmentation is designed, where the lengths of the edges are varying according to the characteristics of each dimension. Finally, a modified local outlier factor (LOF) by using the approximate upper boundary is presented to evaluate the degree of the suspected outliers, and the confirmed ones are identified by collaboratively considering the manufacturing signals. Validation experiments by employing real-world data from a typical steel enterprise are carried out, and the results indicate that the proposed method exhibits reliable performance and high efficiency when facing with the anomaly detection problem of multi-dimensional industrial data." @default.
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- W4385688209 date "2023-11-01" @default.
- W4385688209 modified "2023-10-16" @default.
- W4385688209 title "Varying-scale HCA-DBSCAN-based anomaly detection method for multi-dimensional energy data in steel industry" @default.
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- W4385688209 doi "https://doi.org/10.1016/j.ins.2023.119479" @default.
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