A new method for process monitoring based on mixture probabilistic principal component analysis models

  • Authors:

  • Zhong-Gai Zhao;Fei Liu

  • Affiliations:

  • Institute of Automation, Southern Yangtze University, Wuxi, P.R. China;Institute of Automation, Southern Yangtze University, Wuxi, P.R. China

  • Venue:
  • ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

Conventional PCA-based monitoring method relies on the assumption that process data is normally distributed, which the actual industrial processes often don’t satisfy. Instead, mixture probabilistic principal component analysis (MPPCA) models are suitable to process with any probability density function. But, it suffers a drawback that the needed charts are too many to be watched in practice while the number of sub-models in MPPCA is large. Different from existing MPPCA, this paper proposes a novel method, which integrates every monitoring chart of MPPCA models into only one chart via probability and field process monitoring can rely on just one chart. The application in real chemical separation process shows validity of the proposed method.