ISIJ International
Online ISSN : 1347-5460
Print ISSN : 0915-1559
Regular Article
Fault Diagnosis for Blast Furnace Ironmaking Process Based on Two-stage Principal Component Analysis
Tongshuai ZhangHao YeWei WangHaifeng Zhang
Author information
JOURNALS OPEN ACCESS FULL-TEXT HTML

2014 Volume 54 Issue 10 Pages 2334-2341

Details
Abstract

Monitoring an ironmaking process is a very challenging task as it often fluctuates frequently and lacks of direct measurements. Principal component analysis (PCA) technique has been widely used in various industrial fields, mainly due to its advantage of not requiring the information about the principle knowledge of the process and faults. However, the PCA based application results in ironmaking process are still limited. In this paper, based on the dataset collected from a real blast furnace with a volume of 2000 m3, a fault diagnosis method by incorporating the PCA technique in two stages will be presented. To overcome the adverse effects of the peak-like disturbances caused by switching between two distinct hot-blast stoves, they are identified and removed from the dataset through the first-stage PCA. Experimental results show that our method outperforms the existing algorithm and the operators’ monitoring in detecting the getting cold accident of the blast furnace.

Information related to the author
© 2014 by The Iron and Steel Institute of Japan
Previous article Next article
feedback
Top