Journal
CONTROL ENGINEERING PRACTICE
Volume 47, Issue -, Pages 1-14Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.conengprac.2015.11.006
Keywords
Process monitoring; Fault diagnosis; Blast furnace; Iron-making process; Principal component analysis
Funding
- National Natural Science Foundation of China [61290324, 61490701]
Ask authors/readers for more resources
Incidents happening in the blast furnace will strongly affect the stability and smoothness of the iron making process. Thus far, diagnosis of abnormalities in furnaces still mainly relies on the personal experiences of individual workers in many iron works. In this paper, principal component analysis (PCA)-based algorithms are developed to monitor the iron-making process and achieve early abnormality detection. Because the process exhibits a non-normal distribution and a time-varying nature in the measurement data, a static convex hull-based PCA algorithm (SCHPCA) which replaces the traditional T-2-based abnormality detection logic with the convex hull-based abnormality detection logic, and its moving window version, called the moving window convex hull-based PCA algorithm (MWCHPCA) are proposed, respectively. These two algorithms are tested on the real process data to verify their effectiveness in the early abnormality detection of iron-making process. (C) 2015 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available