Data-driven root cause diagnosis of faults in process industries

Gang LI*, S. Joe QIN, Tao YUAN

*Corresponding author for this work

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

108 Citations (Scopus)


Data driven fault detection and diagnosis methods become more and more attractive in modern industries especially process industries. They can not only guarantee safe operation but also greatly improve product quality. For example, dynamic principal component analysis models and reconstruction based contribution are widely applicable in many occasions. However, there is one issue which does not receive enough attention, namely locating the root cause of a fault when it occurs. In this paper, a framework of root cause location is proposed to address this issue, including both stationary faults and nonstationary faults. A case study on Tennessee Eastman process is used to demonstrate the usage and effectiveness of these approaches. Results show the proposed framework is valid.
Original languageEnglish
Pages (from-to)1-11
Number of pages11
JournalChemometrics and Intelligent Laboratory Systems
Early online date20 Sept 2016
Publication statusPublished - 15 Dec 2016
Externally publishedYes

Bibliographical note

This work was supported by members of Texas-Wisconsin-California Control Consortium (TWCCC), and Center for Interactive Smart Oilfield Technologies (Cisoft). It was also supported by NSFC under grants (61020106003, 61490704, 61333005, 61273173, 61473002, 61473033 and 61673032), the SAPI Fundamental Research of Northeastern University of China (2013ZCX02-01).


  • Dynamic principal component analysis
  • Dynamic time warping
  • Granger causality analysis
  • Reconstruction based contribution
  • Root cause diagnosis


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