Fault detection based on the weight graph method in a blast furnace process

被引：0
作者：
An R.-Q. [1 ]
Yang C.-J. [1 ]
Pan Y.-J. [1 ,2 ]
机构：
[1] Department of Control Science and Engineering, Zhejiang University, Hangzhou
[2] Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang
来源：
Gao Xiao Hua Xue Gong Cheng Xue Bao/Journal of Chemical Engineering of Chinese Universities | 2020年 / 34卷 / 02期
关键词：
Blast furnace; Fault detection; Process control; Weight graph;
D O I：
10.3969/j.issn.1003-9015.2020.02.026
中图分类号：
学科分类号：
摘要：
Since industrial processes are in general complex, proposing robust fault detection and identification is an important task to ensure process safety. In this paper a weight graph based fault detection method is proposed to reduce the influence of the outliers in blast furnace process. The introduced fault detection method has the advantage of being unsupervised and non-parametric. In this method, first the minimum spanning tree of observations is constructed. The weights are calculated by Euclidean distances, and a parameter is introduced to remove the outliers. Next the number of edges, which connect the two observations derived from two group, are counted to detect the fault. The power of proposed method was illustrated through numerical simulation of a blast furnace process. The results show that the weighted graph method can reduce the influence of outliers collected in the data matrix during the blast furnace process and improve the effect of fault detection. © 2020, Editorial Board of Journal of Chemical Engineering of Chinese Universities". All right reserved."
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收藏
页码：495 / 502
页数：7
相关论文
共 30 条
  • [1] GAO C, CHEN J, ZENG J, Et al., A chaos-based iterated multistep predictor for blast furnace ironmaking process, AIChE Journal, 55, 4, pp. 947-962, (2009)
  • [2] PAN Y, YANG C, AN R, Et al., Robust principal component pursuit for fault detection in a blast furnace process, Industrial & Engineering Chemistry Research, 57, 1, pp. 283-291, (2017)
  • [3] ZHOU N, LI S J., R-Vine Copula selection based on kernel density estimation and its application in fault detection, Journal of Chemical Engineering of Chinese Universities, 33, 2, pp. 443-452, (2019)
  • [4] GE Z, SONG Z, GAO F., Review of recent research on data-based process monitoring, Industrial & Engineering Chemistry Research, 52, 10, pp. 3543-3562, (2013)
  • [5] AMINIKHANGHAHI S, COOK D J., A survey of methods for time series change point detection, Knowledge and Information Systems, 51, 2, pp. 339-367, (2017)
  • [6] AN R, YANG C, ZHOU Z, Et al., Comparison of different optimization methods with support vector machine for blast furnace multi-fault classification, IFAC-PapersOnLine, 48, 21, pp. 1204-1209, (2015)
  • [7] HARCHAOUI Z, MOULINES E, BACH F R., Kernel change-point analysis, Advances in neural information processing systems, pp. 609-616, (2009)
  • [8] ROSENBAUM P R., An exact distribution-free test comparing two multivariate distributions based on adjacency, Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67, 4, pp. 515-530, (2005)
  • [9] HAN D, TSUNG F, XIAN J., On the optimality of Bayesian change-point detection, The Annals of Statistics, 45, 4, pp. 1375-1402, (2017)
  • [10] ERIKSSON M, OLOFSSON T., Computationally efficient offline joint change point detection in multiple time series, IEEE Transactions on Signal Processing, 67, 1, pp. 149-163, (2019)
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