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

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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
[1] HAN J, KAMBER M. Data mining: concepts and techniques(the Morgan Kaufmann series in data management systems)[J]. Antimicrobial Agents and Chemotherapy, 2015, 59(3):1435-1440.
[2] YU F X, SUO Y N, ZHANG X, et al. Data mining in blast furnace smelting parameter[J]. Applied Mechanics and Materials, 2013, 303-306: 1093-1096.
[3] 明菲. 关联规则挖掘在高炉炉况预测中的应用研究[D]. 重庆:重庆大学, 2009. MING Fei. Research on application of association rule mining to blast furnace situation prediction[D]. Chongqing: Chongqing University, 2009.
[4] ZHANG T, YE H, WANG W, et al. Fault diagnosis for blast furnace ironmaking process based on two-stage principal component analysis[J]. ISIJ International, 2014, 54(10): 2334-2341.
[5] ZHOU B, YE H, ZHANG H, et al. Process monitoring of iron-making process in a blast furnace with PCA-based methods[J]. Control Engineering Practice, 2016, 47: 1-14.
[6] 苏鑫,吴迎亚,裴华健,等. 大数据技术在过程工业中的应用研究进展[J]. 化工进展, 2016, 35(6):1652-1659. SU Xin, WU Yingya, PEI Huajian, et al. Recent development of the application of big data technology in process industries[J]. Chemical Industry and Engineering Progress, 2016, 35(6):1652-1659.
[7] ZULLO L. Validation and verification of continuous plants operating modes using multivariate statistical methods[J]. Computers & Chemical Engineering, 1996, 20(12): S683-S688.
[8] NATARAJAN S, SRINIVASAN R. Multi-model based process condition monitoring of offshore oil and gas production process[J]. Chemical Engineering Research and Design, 2010, 88(5-6): 572-591.
[9] KRZANOWSKI W J. Between-groups comparison of principal components[J]. Journal of the American Statistical Association, 1979, 74(367): 703-707.
[10] YAO Y, GAO F. Phase and transition based batch process modeling and online monitoring[J]. Journal of Process Control, 2009, 19(5): 816-826.
[11] JOHANNESMEYER M C. Abnormal situation analysis using pattern recognition techniques and historical data[D]. Santa Barbara:University of California, 1999.
[12] 李秀玉, 张成, 逄玉俊. 基于PCA的相似度方法在半导体产品分类中的应用[J]. 沈阳化工大学学报, 2013, 27(1):58-62. LI Xiuyu, ZHANG Cheng, PANG Yujun. Application of PCA similarity factor in classification of semiconductor products[J]. Journal of Shenyang University of Chemical Technology, 2013, 27(1):58-62.
[13] SINGHAL A, SEBORG D E. Pattern matching in multivariate time series databases using a moving-window approach[J]. Industrial & Engineering Chemistry Research, 2002, 41(16): 3822-3838.
[14] 蔡晓妍, 戴冠中, 杨黎斌. 谱聚类算法综述[J]. 计算机科学, 2008, 35(7):14-18. CAI Xiaoyan, DAI Guanzhong, YANG Libin. Survey on spectral clustering algorithms[J]. Computer Science, 2008, 35(7):14-18.
[15] LUXBURG U. A tutorial on spectral clustering[J].Statistics and Computing, 2007,17(4): 395-416.
[16] NG A Y, JORDAN M I, WEISS Y. On spectral clustering: analysis and an algorithm[C] //Neural Information Processing Systems: Natural and Synthetic. Vancouver, Canada:MIT Press, 2001, 14(2): 849-856.
[17] 张亚平. 谱聚类算法及其应用研究[D].太原:中北大学, 2014. ZHANG Yaping. Spectral clustering algorithm and its application research[D]. Taiyuan:North University of China, 2014.
[18] SINGHAL A, SEBORG D E. Clustering multivariate time-series data[J]. Journal of Chemometrics, 2005, 19(8): 427-438.
[19] 窦克勤, 叶昊, 张海峰,等. 基于主元分析的高炉异常炉况检测[J]. 上海交通大学学报, 2015, 49(12):1862-1867. DOU Keqin, YE Hao, ZHANG Haifeng, et al. Fault detection for ironmaking process of blast furnace based on PCA[J]. Journal of Shanghai Jiaotong University, 2015, 49(12):1862-1867.
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