A Modified Principal Component Regression Method for Quality-related Fault Detection

As an effective data dimensionality reduction technique, PCA is widely used in the field of process monitoring. PCR, as an improved method of PCA, obtains the coefficient matrix between input and output by least square regression of score matrix and quality variable. However, the detection effect of PCR on quality-related faults still needs to be improved. Focusing on this issue, a MPCR method for quality-related fault detection is proposed in this paper. Where LU decomposition is introduced to further decompose the coefficient matrix of PCR, decompose process variables into quality-related and quality-independent parts, and design corresponding test statistics to make the algorithm more suitable for modern industrial systems. The effectiveness of the algorithm is verified by a numerical example and the Tennessee Eastman process. The results show that MPCR algorithm has higher fault detection rate and better tracking performance than the traditional PLS.