Ensemble modified independent component analysis for enhanced non-Gaussian process monitoring

As a multivariate statistical tool, the modified independent component analysis (MICA) has drawn considerable attention within the non-Gaussian process monitoring circle since it can solve two main problems in the original ICA method. Despite the diversity in applications, the determination logic for non-quadratic functions involved in the iterative procedures of MICA algorithm has always been empirical. Given that the MICA is an unsupervised modeling method, a direct rational study that can conclusively demonstrate which non-quadratic function is optimal for the general purpose of fault detection is inaccessible. The selection of non-quadratic functions is still a challenge that has rarely been attempted. Recognition of this issue and motivated by the superiority of ensemble learning strategy, a novel ensemble MICA (EMICA) modeling approach is presented for enhancing non-Gaussian process monitoring performance. Instead of focusing on a single non-quadratic function, the proposed method combines multiple base MICA models derived from different non-quadratic functions into an ensemble one, and the Bayesian inference is employed as a decision fusion method to form a unique monitoring index for fault detection. The enhanced fault detectability of the EMICA method is also illustrated on two industrial processes.