JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2017, Vol. 47 ›› Issue (5): 143-149.doi: 10.6040/j.issn.1672-3961.0.2017.172

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Clustering of blast furnace historical data based on PCA similarity factor and spectral clustering

PANG Renming1, WANG Bo1, YE Hao1*, ZHANG Haifeng2, LI Mingliang2   

  1. 1. Department of Automation, Tsinghua University, Beijing 100084, China;
    2. Liuzhou Iron and Steel Co. Ltd., Liuzhou 545002, Guangxi, China
  • Received:2017-02-10 Online:2017-10-20 Published:2017-02-10

Abstract: The principal component analysis(PCA)similarity factor and spectral clustering algorithms were combined and applied analyze the operational state change in a blast furnace by mining the historical data. The similarity between different data sets generated from moving windows by combining the PCA similarity factor and the distance similarity factor was measured, and the historical data were clustered by constructing the graph from the similarity between different data sets and using spectral clustering algorithm. The effect of operating point drift was reduced and the more accurate clustering result was effectively and steadily achieved by the proposed method. The off-line test proved that, compared with the existing methods which combined the PCA similarity factor and k-means clustering, the proposed method could more effectively recognize the operational state change in a blast furnace.

Key words: PCA similarity factor, blast furnace, multimode, operating point drift, spectral clustering, data mining

CLC Number: 

  • TP277
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