A distribution-free method for process monitoring

Traditional multivariate statistical process control methods such as principal component analysis are limited to Gaussian process data when they used for process monitoring. However, the deficiency is not due to the method itself, but lies in the monitoring statistic construction and its confidence limit determination. This paper proposed a distribution-free method, which employs the one-class SVM to construct new monitoring statistics. Thus two new statistics are developed separately in two subspaces of the PCA model: the principal component subspace and the residual subspace. When some fault has been detected, a novel fault reconstruction scheme is proposed. For fault identification, two new identification indices are constructed. The performance of the proposed method in fault detection, reconstruction and identification is evaluated through a case study of the Tennessee Eastman (TE) benchmark process.

Research highlights
► A one-class support vector machine based method is proposed for process monitoring.
► The proposed method is distribution-free for process data.
► Two monitoring statistics are constructed in the two subspaces of the PCA model.
► A fault reconstruction method is developed for identification of the detected fault.
► The effectiveness of the proposed method is confirmed through a benchmark process.