Quality-related Fault Detection Approach Based on Dynamic Total Principal Component Regression Component Regression

WANG Guang; SUN Chengyuan; YIN Shen

doi:10.13976/j.cnki.xk.2017.0671

Volume 46 Issue 6

Dec. 2017

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INFORMATION AND CONTROL > 2017 > 46(6): 671-676. > DOI: 10.13976/j.cnki.xk.2017.0671 CSTR: 32166.14.xk.2017.0671

WANG Guang, SUN Chengyuan, YIN Shen. Quality-related Fault Detection Approach Based on Dynamic Total Principal Component Regression Component Regression[J]. INFORMATION AND CONTROL, 2017, 46(6): 671-676. DOI: 10.13976/j.cnki.xk.2017.0671

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Quality-related Fault Detection Approach Based on Dynamic Total Principal Component Regression Component Regression

1.
College of Engineering, Bohai University, Jinzhou 121000, China
2.
School of Astronautics, Harbin Institute of Technology, Harbin 150001, China

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Received Date: November 08, 2016
Revised Date: February 19, 2017
Accepted Date: January 10, 2017
Available Online: December 01, 2022
Published Date: December 19, 2017

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Abstract

Abstract

On the basis of the structure of auto-regressive moving average exogenous (ARMAX), we propose a dynamic total principal component regression (DT-PCR) method for dynamic performance of quality-related fault detection. We form the input augmented matrix in the method based on the delay value of the input. The augmented matrix is divided into two orthogonal parts, namely, quality-related and quality-unrelated. We design a simple fault detection strategy based on statistics in two subspaces that correspond to the two parts. The output prediction accuracy of DT-PCR is better than that of former methods. The prediction and fault detection performance of the proposed approach are proved by a numerical example and the Tennessee Eastman process through a comparison by using total partial least squares (TPLS).
- fault detection,
- output prediction,
- quality-related,
- dynamic system

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Quality-related Fault Detection Approach Based on Dynamic Total Principal Component Regression Component Regression

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