Dynamic time warping based causality analysis for root-cause diagnosis of nonstationary fault processes

Gang LI, Tao YUAN, S. Joe QIN*, Tianyou CHAI

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

20 Citations (Scopus)


It is very important to diagnose abnormal events in industrial processes. Based on normal operating data in a dynamic process, dynamic latent variable model provides a clear view of separating dynamic and static variations. Recent work by Li et al. (2014a) has shown an effective diagnosis in faulty variables with multidirectional reconstruction based contributions. Their further work took Granger causality analysis into accounts to explore the casual relations instead of only correlations. Although Granger causality is a widely used method for many applications, it needs time series to be stationary to calculate the causality index, which is not applicable for nonstationary fault processes. In this paper, a new causality analysis index based on dynamic time warping is proposed to determine the causal direction between pairs of faulty variables. The case study on the Tennessee Eastman process with a step fault shows the effectiveness of the proposed approach.
Original languageEnglish
Pages (from-to)1288-1293
Number of pages6
Issue number8
Early online date25 Sept 2015
Publication statusPublished - 2015
Externally publishedYes
Event9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015 - , Canada
Duration: 7 Jun 201510 Jun 2015


  • Causality analysis
  • Dynamic latent variable model
  • Dynamic time warping
  • Multi-directional reconstruction based contribution
  • Root cause diagnosis
  • Wavele denoising


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