基于PCA相似度和谱聚类相结合的高炉历史数据聚类

doi:10.6040/j.issn.1672-3961.0.2017.172

山东大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (5): 143-149.doi: 10.6040/j.issn.1672-3961.0.2017.172

基于PCA相似度和谱聚类相结合的高炉历史数据聚类

庞人铭¹,王波¹,叶昊^1*,张海峰²,李明亮²

1. 清华大学自动化系, 北京 100084;2. 广西柳州钢铁(集团)公司, 广西柳州 545002

收稿日期:2017-02-10 出版日期:2017-10-20 发布日期:2017-02-10
通讯作者: 叶昊(1969— ),男,天津人,博士生导师,工学博士,主要研究方向为动态系统故障诊断.E-mail:haoye@tsinghua.edu.cn E-mail:prm14@mails.tsinghua.edu.cn
作者简介:庞人铭(1992— ),男,湖南常德人,硕士研究生,主要研究方向为过程监控和故障诊断.E-mail:prm14@mails.tsinghua.edu.cn
基金资助:
国家自然科学基金资助项目(61290324)

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

PANG Renming¹, WANG Bo¹, YE Hao^1*, ZHANG Haifeng², LI Mingliang²

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

摘要/Abstract

摘要： 将主元分析(principal component analysis, PCA)模型相似度(以下简称PCA相似度)和谱聚类(spectral clustering)算法相结合,并用于基于高炉历史数据挖掘的炉况工作点变化的分析。利用PCA相似度与距离相似度的加权来衡量滑窗数据集之间的相似度,进一步将数据集的聚类问题转化为图的最优划分问题,通过谱聚类得到聚类结果。该方法降低了高炉工作点漂移的影响,能够有效稳定的实现高炉炉况工作点的聚类。基于现场历史数据的离线测试表明:与已有的基于PCA相似度和k-means聚类的算法对比,本研究可以更加有效区分炉况工作点的跳变。

关键词: 谱聚类, 高炉, 多工况, 工作点漂移, PCA相似度, 数据挖掘

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

中图分类号:

TP277

庞人铭,王波,叶昊,张海峰,李明亮. 基于PCA相似度和谱聚类相结合的高炉历史数据聚类[J]. 山东大学学报(工学版), 2017, 47(5): 143-149.

PANG Renming, WANG Bo, YE Hao, ZHANG Haifeng, LI Mingliang. Clustering of blast furnace historical data based on PCA similarity factor and spectral clustering[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(5): 143-149.

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基于PCA相似度和谱聚类相结合的高炉历史数据聚类

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

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