Unevenly Sampled Dynamic Data Modeling and Monitoring With an Industrial Application

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

22 Scopus Citations
View graph of relations

Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2203-2213
Number of pages11
Journal / PublicationIEEE Transactions on Industrial Informatics
Volume13
Issue number5
Online published2 May 2017
Publication statusPublished - Oct 2017
Externally publishedYes

Abstract

In this paper, a dynamic modeling method for unevenly sampled data is proposed for the monitoring of bi-layer (i.e., a process layer and a quality layer) dynamic processes. First, a novel uneven data dynamic canonical correlation analysis method with an integrated dynamic time window is proposed for interlayer latent structure modeling, which captures the dynamic relations between regularly sampled process data and quality data with slow and irregular sampling. The new model is a step toward big data modeling to deal with data irregularity and diversity. Second, after extracting covariations using an interlayer model, intralayer variations are extracted using subsequent principal component analysis on the residual subspaces of the original process data and quality data, respectively. Third, a concurrent monitoring method for unevenly sampled bi-layer data is proposed. Finally, the proposed method is demonstrated using an illustrative simulation example and applied successfully to a real blast furnace iron-making process.

Research Area(s)

  • Dynamic processes, dynamic canonical correlation analysis (CCA), interlayer and intralayer concurrent monitoring, unevenly sampled data modeling