Quality-relevant fault monitoring based on efficient projection to latent structures with application to hot strip mill process

2015 ◽  
Vol 9 (7) ◽  
pp. 1135-1145 ◽  
Author(s):  
Kaixiang Peng ◽  
Kai Zhang ◽  
Bo You ◽  
Jie Dong
Keyword(s):  
Hot Strip Mill ◽  
Projection To Latent Structures ◽  
Fault Monitoring ◽  
Hot Strip ◽  
Latent Structures ◽  
Mill Process

scholarly journals Quality-Related Process Monitoring Based on Total Kernel PLS Model and Its Industrial Application

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Kaixiang Peng ◽  
Kai Zhang ◽  
Gang Li
Keyword(s):  
Process Monitoring ◽  
Hot Strip Mill ◽  
Tennessee Eastman Process ◽  
Present Scheme ◽  
Hot Strip ◽  
Latent Structures ◽  
Related Process ◽  
Mill Process ◽  
Advanced Version

Projection to latent structures (PLS) model has been widely used in quality-related process monitoring, as it can establish a mapping relationship between process variables and quality index variables. To enhance the adaptivity of PLS, kernel PLS (KPLS) as an advanced version has been proposed for nonlinear processes. In this paper, we discuss a new total kernel PLS (T-KPLS) for nonlinear quality-related process monitoring. The new model divides the input spaces into four parts instead of two parts in KPLS, where an individual subspace is responsible in predicting quality output, and two parts are utilized for monitoring the quality-related variations. In addition, fault detection policy is developed based on the T-KPLS model, which is more well suited for nonlinear quality-related process monitoring. In the case study, a nonlinear numerical case, the typical Tennessee Eastman Process (TEP) and a real industrial hot strip mill process (HSMP) are employed to access the utility of the present scheme.

A new fault detection scheme based on canonical independent component analysis with application to hot strip mill process

2020 ◽  
pp. 095965182095821
Author(s):  
Rui-Cheng Zhang ◽  
Yu-Ting Li ◽  
Wei-Zheng Liang ◽  
Wei Xiong
Keyword(s):  
Fault Detection ◽  
Independent Component Analysis ◽  
Canonical Variable ◽  
Component Analysis ◽  
Independent Component ◽  
Hot Strip Mill ◽  
Process Data ◽  
Detection Scheme ◽  
Hot Strip ◽  
Mill Process

Aiming at the problems of inaccurate fault detection and error alarm in the process of hot strip mill process, a fault detection scheme of canonical independent component analysis is proposed. The new scheme first uses canonical variable analysis to calculate the canonical variable matrix of observation data, which effectively solves the problem of autocorrelation and cross-correlation. Then the canonical variable matrix is decomposed by independent component analysis to obtain independent elements. Finally, the data are monitored online through constructing statistics. It is proved that the accuracy of the scheme for identifying fault data is reached to 100%, and the misjudgment rate data are reduced to less than 0.6% through the simulation study of the hot strip mill process data.

scholarly journals Unit-level modelling for KPI of batch hot strip mill process using dynamic partial least squares

2015 ◽  
Vol 48 (28) ◽  
pp. 1005-1010 ◽  
Author(s):  
Kai Zhang ◽  
Yuri A.W Shardt ◽  
Zhiwen Chen ◽  
Steven X Ding ◽  
Kaixiang Peng
Keyword(s):  
Least Squares ◽  
Partial Least Squares ◽  
Hot Strip Mill ◽  
Unit Level ◽  
Hot Strip ◽  
Mill Process
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