A Novel Sliding Window PCA-IPF based Steady-State Detection Framework and Its Industrial Application

Yalin Wang, Kenan Sun, Xiaofeng Yuan, Yue Cao, Ling Li, Heikki Koivo

Research output: Contribution to journalArticleScientificpeer-review

18 Citations (Scopus)
124 Downloads (Pure)


In industrial processes, it is of great significance to carry out steady-state detection (SSD) for effective system modeling, operation optimization, performance evaluation and process monitoring. Traditional SSD approaches often need to identify process state for each variable and obtain a composite index with sliding window technique, which ignores the variable correlations and is time consuming. Moreover, they can only provide the state of each whole window that slides along data series. To deal with these problems, a novel sliding window PCA-IPF (principal component analysis-improved polynomial fitting) based method is proposed for steady-state detection. In the proposed framework, principal component analysis is first used to eliminate the data correlations and variable noises. Then, the size of sliding window is automatically determined by the data series of the first principal component. After that, SSD is carried out for each selected principal component by 2nd-order improved polynomial fitting. At last, the overall process state is determined by the weighted combination of the SSD results of selected principal components, in which the weight of each principal component is determined by the its corresponding contribution of variance. The effectiveness and flexibility of the proposed SSD framework is validated on an industrial hydrocracking process.

Original languageEnglish
Pages (from-to)20995-21004
Number of pages9
JournalIEEE Access
Publication statusPublished - 2018
MoE publication typeA1 Journal article-refereed


  • Correlation
  • Fitting
  • Hydrocracking process
  • Market research
  • Microsoft Windows
  • Polynomial fitting
  • Principal component analysis
  • Process control
  • Sliding window
  • Steady-state
  • Steady-state detection


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