Abstract
In multivariate statistical process monitoring (MSPC), isolation of faulty variables is a critical step to discover the source of the detected fault. Although fault detection methods have been intensively investigated, studies on fault isolation are relatively limited, due to the difficulty in analyzing the influences of multiple variables on monitoring statistics. To solve the problems of the existing methods, this paper proposes to conduct fault isolation via a lasso-based penalized discriminant analysis technique. Instead of just offering a suggested set of faulty variables, the proposed method provides more information on the relevance of process variables to the detected fault, which facilitates the subsequent root cause diagnosis step after isolation. The benchmark Tennessee Eastman (TE) process is used as a case study to illustrate the effectiveness of the proposed method.