Research Article
An Improved Regression Partial Least Squares Method for Quality-Related Process Monitoring of Industrial Control Systems
@INPROCEEDINGS{10.1007/978-3-031-34899-0_6, author={Zhiqiang Zhang and Wenxiao Gao and Danlu Yu and Aihua Zhang}, title={An Improved Regression Partial Least Squares Method for Quality-Related Process Monitoring of Industrial Control Systems}, proceedings={Sensor Systems and Software. 13th EAI International Conference, S-Cube 2022, Dalian, China, December 7-9, 2022, Proceedings}, proceedings_a={S-CUBE}, year={2023}, month={6}, keywords={PLS PCR EWMA IRPLS TE}, doi={10.1007/978-3-031-34899-0_6} }- Zhiqiang Zhang
Wenxiao Gao
Danlu Yu
Aihua Zhang
Year: 2023
An Improved Regression Partial Least Squares Method for Quality-Related Process Monitoring of Industrial Control Systems
S-CUBE
Springer
DOI: 10.1007/978-3-031-34899-0_6
Abstract
Partial least squares (PLS) is a widely used and effective method in the field of fault detection. However, due to the fact that the standard PLS decomposes the process variable space into two subspaces which are not completely orthogonal, it is insufficient in quality-related fault detection. To solve this problem, principal component regression (PCR) is used to decompose the quality variables of PLS model and realize the reconstruction of the process variable space. In this way, the process variable space is decomposed into highly correlated and highly irrelevant parts of quality variables, and the two are monitored by designing statistics respectively. Furthermore, an adaptive threshold based on the idea of exponential weighted moving average (EWMA) is introduced to reduce the false positives and missed positives caused by the traditional fixed threshold, and this method is named as improved regression partial least squares (RPLS). Finally, linear and nonlinear numerical examples and Tennessee Eastman (TE) processes are used to verify the effectiveness of the proposed method which named improved regression partial least squares (IRPLS). Finally, linear and nonlinear numerical cases and Tennessee Eastman (TE) processes are used to verify the effectiveness of IRPLS. The results show that the proposed method can effectively improve the fault detection rate and algorithm follow-through performance, and reduce false positives.
