Ensemble modified independent component analysis for enhanced non-Gaussian process monitoring. (January 2017)

• Record Type: Journal Article
• Authors: Tong, Chudong; Lan, Ting; Shi, Xuhua
• Is Part Of: Control engineering practice. Volume 58(2017)
• Page Start: 34
• Page End: 41
• Publication Date: 2017-01
• Subjects: Modified independent component analysis -- Fault detection -- Ensemble learning; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2016.09.014
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: 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. Highlights: A novel MICA-based method is proposed for enhanced non-Gaussian process monitoring. Multiple MICA base monitoring results are combined into an ensemble decision. Robust performance is ensured regardless of the selection of non-quadratic functions. Case studies demonstrate the priority and the promise of the proposed EMICA model.