High-dimensional, slow-time-varying process monitoring technique based on adaptive eigen subspace extraction method. (September 2022)

• Record Type: Journal Article
• Authors: Feng, Xiaowei; Kong, Xiangyu; He, Chuan; Luo, Jiayu
• Is Part Of: Journal of process control. Volume 117(2022)
• Page Start: 122
• Page End: 131
• Publication Date: 2022-09
• Subjects: Process monitoring -- Principal component analysis -- Eigen subspace extraction -- Fault reconstruction -- Adaptive algorithm; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2022.07.009
• Languages: English
• ISSNs: 0959-1524

Abstract

Abstract: In this paper, in order to monitor the slow-time-varying industrial process, an adaptive method is proposed based on the neural network model and fault reconstruction method. Firstly, a unified neural network algorithm is introduced to extract the principal and minor eigen subspace with low computational complexity, and the whole eigenspace is divided into three partitions to further reduce the complexity of high-dimensional data computation. Then, the process is monitored based on a combined statistic index and the corresponding adaptive threshold. Moreover, the eigen subspace can still be updated even when in a faulty case. Finally, computer simulation confirms the capacity of the proposed method for high-dimensional, slow-time-varying process monitoring. Highlights: Available for high-dimensional, slow-time-varying industrial process monitoring. The whole eigenspace is divided into three partitions for complexity reduction. The combined statistic index helps simplify the monitoring procedures. Fault reconstruction method is helpful for eigen subspace updating in faulty case.