Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model

Le ZHOU; Zhiqiang GE; Zhihuan SONG; S. Joe QIN

doi:10.1109/SAFEPROCESS45799.2019.9213443

Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model

Le ZHOU, Zhiqiang GE, Zhihuan SONG, S. Joe QIN

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

Abstract

For dynamic process modeling and monitoring purpose, it is desirable to extract both the auto-correlations and the cross-correlations in measurements. Besides, the proposed dynamic model is also expected to provide an accurate prediction, visualization and an explicit interpretation of the data structures. From this perspective, the dynamic latent variable model is more suitable compared to the traditional dynamic principal component analysis (DPCA). In this paper, a novel independent dynamic latent variable model is proposed to explicitly extract several independent dynamic latent variables with which to capture process dynamics in the measurements. The proposed model is derived in the probabilistic framework and the model parameters are estimated via the expectation-maximum algorithm. Finally, a case study is illustrated to evaluate the performance of the proposed method for dynamic modeling and process monitoring.

Original language	English
Title of host publication	Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019
Publisher	Institute of Electrical and Electronics Engineers
Pages	589-595
Number of pages	7
ISBN (Electronic)	9781728106816
ISBN (Print)	9781728106823
DOIs	https://doi.org/10.1109/SAFEPROCESS45799.2019.9213443
Publication status	Published - Jul 2019
Externally published	Yes
Event	11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS 2019) - Xiamen, China Duration: 5 Jul 2019 → 7 Jul 2019

Conference

Conference	11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS 2019)
Country/Territory	China
City	Xiamen
Period	5/07/19 → 7/07/19

Bibliographical note

This work is supported by the National Natural Science Foundation of China (61603342), NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (U1609214) and Zhejiang Provincial Natural Science Foundation of China (LY19F030003).

Keywords

Dynamic modelling
Independent Dynamic Latent Variable
Process Monitoring

Access to Document

10.1109/SAFEPROCESS45799.2019.9213443

Cite this

ZHOU, L., GE, Z., SONG, Z., & QIN, S. J. (2019). Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model. In Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019 (pp. 589-595). Article 9213443 Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/SAFEPROCESS45799.2019.9213443

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title = "Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model",

abstract = "For dynamic process modeling and monitoring purpose, it is desirable to extract both the auto-correlations and the cross-correlations in measurements. Besides, the proposed dynamic model is also expected to provide an accurate prediction, visualization and an explicit interpretation of the data structures. From this perspective, the dynamic latent variable model is more suitable compared to the traditional dynamic principal component analysis (DPCA). In this paper, a novel independent dynamic latent variable model is proposed to explicitly extract several independent dynamic latent variables with which to capture process dynamics in the measurements. The proposed model is derived in the probabilistic framework and the model parameters are estimated via the expectation-maximum algorithm. Finally, a case study is illustrated to evaluate the performance of the proposed method for dynamic modeling and process monitoring.",

keywords = "Dynamic modelling, Independent Dynamic Latent Variable, Process Monitoring",

author = "Le ZHOU and Zhiqiang GE and Zhihuan SONG and QIN, {S. Joe}",

note = "This work is supported by the National Natural Science Foundation of China (61603342), NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (U1609214) and Zhejiang Provincial Natural Science Foundation of China (LY19F030003).; 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS 2019) ; Conference date: 05-07-2019 Through 07-07-2019",

year = "2019",

month = jul,

doi = "10.1109/SAFEPROCESS45799.2019.9213443",

language = "English",

isbn = "9781728106823",

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booktitle = "Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019",

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ZHOU, L, GE, Z, SONG, Z & QIN, SJ 2019, Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model. in Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019., 9213443, Institute of Electrical and Electronics Engineers, pp. 589-595, 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS 2019), Xiamen, China, 5/07/19. https://doi.org/10.1109/SAFEPROCESS45799.2019.9213443

Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model. / ZHOU, Le; GE, Zhiqiang; SONG, Zhihuan et al.
Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019. Institute of Electrical and Electronics Engineers, 2019. p. 589-595 9213443.

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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AU - ZHOU, Le

AU - GE, Zhiqiang

AU - SONG, Zhihuan

AU - QIN, S. Joe

N1 - This work is supported by the National Natural Science Foundation of China (61603342), NSFC-Zhejiang Joint Fund for the Integration of Industrialization and Informatization (U1609214) and Zhejiang Provincial Natural Science Foundation of China (LY19F030003).

PY - 2019/7

Y1 - 2019/7

N2 - For dynamic process modeling and monitoring purpose, it is desirable to extract both the auto-correlations and the cross-correlations in measurements. Besides, the proposed dynamic model is also expected to provide an accurate prediction, visualization and an explicit interpretation of the data structures. From this perspective, the dynamic latent variable model is more suitable compared to the traditional dynamic principal component analysis (DPCA). In this paper, a novel independent dynamic latent variable model is proposed to explicitly extract several independent dynamic latent variables with which to capture process dynamics in the measurements. The proposed model is derived in the probabilistic framework and the model parameters are estimated via the expectation-maximum algorithm. Finally, a case study is illustrated to evaluate the performance of the proposed method for dynamic modeling and process monitoring.

AB - For dynamic process modeling and monitoring purpose, it is desirable to extract both the auto-correlations and the cross-correlations in measurements. Besides, the proposed dynamic model is also expected to provide an accurate prediction, visualization and an explicit interpretation of the data structures. From this perspective, the dynamic latent variable model is more suitable compared to the traditional dynamic principal component analysis (DPCA). In this paper, a novel independent dynamic latent variable model is proposed to explicitly extract several independent dynamic latent variables with which to capture process dynamics in the measurements. The proposed model is derived in the probabilistic framework and the model parameters are estimated via the expectation-maximum algorithm. Finally, a case study is illustrated to evaluate the performance of the proposed method for dynamic modeling and process monitoring.

KW - Dynamic modelling

KW - Independent Dynamic Latent Variable

KW - Process Monitoring

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M3 - Conference paper (refereed)

AN - SCOPUS:85094662259

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BT - Proceedings of 2019 11th CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes, SAFEPROCESS 2019

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Y2 - 5 July 2019 through 7 July 2019

ER -

Dynamic Processes Modeling and Monitoring based on a Novel Dynamic Latent Variable Model

Abstract

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Bibliographical note

Keywords

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