An analytical partial least squares method for process monitoring

Partial least squares (PLS) is an algorithm commonly used for key performance indicator (KPI) industrial process monitoring in recent years. However, there are many shortcomings in PLS, such as uncertainty of the optimization solution, an imperfect optimization goal, and information impurity. To overcome these shortcomings, an analytical PLS (APLS) method is proposed in this study. APLS fully analyzes the correlation between process variables and quality variables, and is solved by an analytic solution to avoid the large computational complexity brought by iterative calculations. A computational complexity analysis of PLS and APLS is performed to verify the advantages of APLS in terms of computational complexity compared to PLS. To better further study the information impurity existing in PLS, we present the proof related to this problem. Moreover, in order to verify the effectiveness of APLS, a numerical example and the thermal power plant process are utilized. It can be seen that the proposed method has a better detection performance compared with existing PLS-related methods.

Automated summary

This study proposes an analytical partial least squares (APLS) method for key performance indicator (KPI) industrial process monitoring. By fully analyzing the correlation between process variables and quality variables, APLS solves the problem through an analytic solution, avoiding the computational complexity of iterative calculations. A computational complexity analysis demonstrates the advantages of APLS over PLS. Additionally, APLS shows better detection performance compared to existing PLS-related methods.