Fault Detection using Empirical Mode Decomposition based PCA and CUSUM with Application to the Tennessee Eastman Process

Yuncheng Du, Dongping Du

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this work, a new algorithm is developed to identify stochastic faults in the Tennessee Eastman (TE) process, which integrates Ensemble Empirical Mode Decomposition (EEMD), Principal Component Analysis (PCA), Cumulative Sum (CUSUM), and half-normal probability plot to detect three particular faults that could not be properly detected with previously reported techniques. This algorithm includes three steps: measurements pre-filtering, sensitivity analysis, and fault detection. Measured variables are first decomposed into different scales using the EEMD-based PCA for extracting fault signatures, from which a subset of variables that are sensitive to faults are selected with the half-normal probability plot. Based on the specific variables, CUSUM-based statistics are further used for improved fault detection. The algorithm can successfully identify three particular faults in the TE process with small time delay.

Original languageEnglish
Pages (from-to)488-493
Number of pages6
JournalIFAC-PapersOnLine
Volume51
Issue number18
DOIs
StatePublished - Jan 1 2018

Keywords

  • Process monitoring
  • control
  • process data analytics
  • sensitivity analysis
  • stochastic faults

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