Multiphase batch process monitoring based on higher‐order cumulant analysis

Abstract

In this paper, a two‐step phase partitioning strategy is proposed. Firstly, the number of phases is automatically determined according to the intra‐class and inter‐class similarity of feature space data, thus avoiding excessive manual intervention. Secondly, the phases are partitioned by step‐wise adding the kernel entropy extended load matrix (KEELM), avoiding the wrong division of phases caused by unstable state of working condition conversion. A process monitoring model based on multiway kernel entropy independent component analysis (MKEICA) is constructed in each sub‐phase to deal with complex batch processes with nonlinear and non‐Gaussian properties. A new statistics index based on the idea of high order cumulant analysis (HCA) is constructed in each sub‐phase for process monitoring. Compared with the traditional second‐order statistics, it can obtain high‐order statistical information. Finally, the proposed method is applied to the penicillin simulation platform process and compared with the traditional multiway kernel independent components analysis (MKICA) and HCA methods to verify the effectiveness of the method that is mentioned above.

Uncorrelated discriminant graph embedding for fault classification

“…With the increasing complexity of industrial systems, data-based methods have been paid more and more attention in multivariate statistical process monitoring (MSPM). [10][11][12][13] Data-based methods have been proved more appropriate for complicated industrial processes. [14] The high-dimensional multivariate data often collected from different sensors.…”

Soft sensor development based on improved just‐in‐time learning and relevant vector machine for batch processes

“…As a modern production method, the batch process is widely used in the semiconductor, biopharmaceutical, and chemical industries. [1][2][3] The demand for products is moving towards multi-variety and high quality, and the complexity of batch processes is increasing because of the development of modern industry. Therefore, it is necessary to obtain online measurement data of batch process quality variables for the safety of batch processes and the stability of product quality.…”