Quality relevant fault detection of batch process via statistical pattern and regression coefficient

Abstract

Compared with the normal process, the statistical values or the relationships between process variables and the quality that represent process situations would change in an abnormal batch. A novel method named quality relevant fault detection based on statistical pattern and regression coefficients (SPRC) is proposed for the batch process. Firstly, the statistical patterns of the process data, such as mean value and SD, are computed to quantify process characteristics. The regression model is built via linear methods, such as multiple linear regression, least absolute shrinkage and selection operator, to describe the linear relationship. The mutual information between the quality and the process parameters is used to characterize the nonlinear relationship. In this way, statistical patterns and regression coefficients which express batch information constitute the two-way matrix. Then, the matrix is dimensionally reduced by related method, such as principal component analysis. The relationships hidden in batch process could be sought by SPRC. Finally, two cases, penicillin fermentation and steel hot rolling, are used to validate the feasibility and advantages of the proposed method.

Quality-related batch process monitoring based on multi-way orthogonal signal correction enhanced total principal component regression

“…f g obey the overall distribution density f(x), given the kernel function K(x), the estimated density of the sample data is expressed as equation (18).…”

“…Zhu et al 17 proposed concurrent CCA to monitor process-related and quality-related faults respectively. Considering the changes of statistical values or relationships between quality variables and process variables, a quality-related fault detection method based on the statistical model and regression coefficient 18 was proposed, linear regression coefficient and mutual information are used to establish the nonlinear and linear relationship between quality variables and process variables. Wang et al 19 proposed a method to divide KPI-related process variables into linear and nonlinear parts by using OSC, and established a monitoring model for the nonlinear part.…”