Improved Statistical Pattern Analysis Monitoring for Complex Multivariate Processes Using Empirical Likelihood

This article developed an improved statistical pattern analysis (SPA) monitoring strategy for fault detection of complex multivariate processes using empirical likelihood. The technique based on statistical pattern analysis performs fault detection by inspecting change in the statistics of process v...

Full description

Saved in:
Bibliographic Details
Published in:Processes 2020-12, Vol.8 (12), p.1619
Main Authors: Shao, Jianwen, Zhang, Xin, Chen, Wenhua, Shen, Xiaomin
Format: Article
Language:English
Subjects:
Batch processes
Confidence limits
Correlation coefficients
Data processing
Empirical analysis
Fault detection
Gaussian process
Hypotheses
Hypothesis testing
Kurtosis
Lagrange multiplier
Monitoring
Multivariate analysis
Pattern analysis
Principal components analysis
Process controls
Process variables
Statistical analysis
Statistics
Variables
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This article developed an improved statistical pattern analysis (SPA) monitoring strategy for fault detection of complex multivariate processes using empirical likelihood. The technique based on statistical pattern analysis performs fault detection by inspecting change in the statistics of process variables (e.g., mean value, correlation coefficient, variance, kurtosis, etc.). It is capable of monitoring non-Gaussian or even nonlinear processes. However, the original SPA framework explicitly computes all the high-order statistics, which significantly increases the scale and dimensionality of the problem, especially in the case of complex multivariate processes. To alleviate this difficulty, we propose monitoring changes in the statistics with the same order using empirical likelihood, which is a widely used estimation method to construct confidence limits or regions for parameters with similar properties. As a result, changes in statistics of the same order can be translated into a single index; hence more information on the faulty conditions can be observed. Furthermore, by considering statistics of the same order, the scale of the problem is reduced significantly. The improved statistical pattern analysis monitoring strategy is suitable for monitoring complex multivariate processes. The performance of the improved method is illustrated by an application study to fault detection of the Tennessee Eastman (TE) process.
ISSN:2227-9717
2227-9717
DOI:10.3390/pr8121619