Canadian Journal of Chemical Engineering, Vol.96, No.2, 444-454, 2018
MODIFIED PARTIAL LEAST SQUARE FOR DIAGNOSING KEY-PERFORMANCE-INDICATOR-RELATED FAULTS
Standard partial least square (PLS) is a useful tool for process monitoring; however, it still encounters some problems for the diagnosis of key performance indicator (KPI) faults. One of its recent modifications, improved PLS (IPLS), decomposes the process measurements into KPI-related and KPI-unrelated parts according to the correlation matrix obtained from the standard PLS. The entire residual space of PLS is categorized as the IPLS's KPI-unrelated part. However, the residual space still involves some information related to KPI, and hence IPLS's decomposition may be inappropriate. In this study, a new modified PLS is proposed, which also decomposes the residual space according to the KPI. The loadings of input data are first decomposed to obtain a projection model. Next, the input data are more appropriately decomposed into KPI-related and KPI-unrelated parts. Correspondingly, two statistic indices can be designed for fault diagnosis. A numerical example and the Tennessee-Eastman (TE) benchmark process are utilized to demonstrate the effectiveness and advantages of the proposed approach.