Fault Detection for Batch Processes Based on Segmentation MPCA

Abstract:

Aiming at the dynamic characteristic of batch production process changes fast and the accurate modeling of it is difficult, so this paper proposes an intermittent fault detection method of the principal component analysis based on process segment. According to the different dynamic characteristics of process data, the process is divided into multiple stages, with the method of piecewise linear approximation of nonlinear modeling to model different stages of the process, in order to make up the deficiency of traditional MPCA fault diagnosis methods. Through the fault detection of the beer fermentation process experiments to verify that the method can detect process faults promptly and improve the speed and accuracy of process monitoring.

• Periodical: Advanced Materials Research (Volumes 1030-1032)
• Pages: 1701-1708
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1030-1032.1701
• Online since: September 2014
• Authors: Ning Lv, Guang Yuan Bai*, Lei Lei Jiang
• Keywords: Batch Process, Fault Detection, Multiway Principal Component Analysis, Piecewise Modeling

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