Autoregressive Dynamic Latent Variable Models for Process Monitoring

Le ZHOU, Gang LI, Zhihuan SONG, S. Joe QIN*

*Corresponding author for this work

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

87 Citations (Scopus)


In most industrial processes, both autocorrelations and cross correlations in the data need to be extracted for the purpose of process monitoring and diagnosis. However, traditional dynamic modeling methods focus on the dynamic relationship while the cross correlations are at best implicit. In this brief, a new autoregressive dynamic latent variable model is proposed to capture both dynamic and static relationships simultaneously. The proposed method is a rather general dynamic model which can improve the performance of modeling and process monitoring. The Kalman filter and smoother are employed for inference while model parameters are estimated with an expectation-maximization algorithm. The corresponding fault detection method is also developed and a numerical example and the Tennessee Eastman benchmark process are used to evaluate the performance of the proposed model.
Original languageEnglish
Article number7457350
Pages (from-to)366-373
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Issue number1
Early online date20 Apr 2016
Publication statusPublished - 2017
Externally publishedYes

Bibliographical note

This work was supported in part by the National Basic Research Program (973 Program) of China under Grant 2012CB720505 and in part by the National Natural Science Foundation of China under Grant 61020106003, Grant 61333005, Grant 61473002, Grant 61490704, and Grant 61573308.


  • Autoregressive dynamic latent variable (ARDLV) models
  • dynamic modeling
  • expectation-maximization (EM) algorithm
  • process monitoring


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