Probabilistic Monitoring of Correlated Sensors for Nonlinear Processes in State Space

Shunyi Zhao; Yuriy S. Shmaliy; Choon Ki Ahn; Chunhui Zhao

doi:10.1109/TIE.2019.2907505

Probabilistic Monitoring of Correlated Sensors for Nonlinear Processes in State Space

Shunyi Zhao, Yuriy S. Shmaliy, Choon Ki Ahn, Chunhui Zhao

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

To optimize control and/or state estimation of industrial processes, information about measurement quality provided by sensors is required. In this paper, a probabilistic scheme is proposed in discrete-time nonlinear state space with the purpose of sensor monitoring. A quantitative index representing the measurement quality, as well as satisfied state estimates, is obtained by estimating the probability density functions (PDFs) of the states and the measurement noise covariance considered as a random variable using the variational Bayesian approach. To solve the intractable integrals of nonlinear PDFs in real time, a set of weighted particles is generated to overlap an empirical density of state, while the PDF of the measurement noise is still derived analytically. An example of localization and an experiment with a rotary flexible joint are supplied to demonstrate that the proposed algorithm significantly improves the applicability of existing methods and can monitor correlated sensors satisfactorily.

Original language	English
Article number	8678661
Pages (from-to)	2294-2303
Number of pages	10
Journal	IEEE Transactions on Industrial Electronics
Volume	67
Issue number	3
DOIs	https://doi.org/10.1109/TIE.2019.2907505
Publication status	Published - 2020 Mar

Keywords

Nonlinear process
particle approximation
sensor monitoring
state estimation
variational Bayesian (VB) inference

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

Access to Document

10.1109/TIE.2019.2907505

Cite this

@article{9c12431f4bf64e498a9507253056295b,

title = "Probabilistic Monitoring of Correlated Sensors for Nonlinear Processes in State Space",

abstract = "To optimize control and/or state estimation of industrial processes, information about measurement quality provided by sensors is required. In this paper, a probabilistic scheme is proposed in discrete-time nonlinear state space with the purpose of sensor monitoring. A quantitative index representing the measurement quality, as well as satisfied state estimates, is obtained by estimating the probability density functions (PDFs) of the states and the measurement noise covariance considered as a random variable using the variational Bayesian approach. To solve the intractable integrals of nonlinear PDFs in real time, a set of weighted particles is generated to overlap an empirical density of state, while the PDF of the measurement noise is still derived analytically. An example of localization and an experiment with a rotary flexible joint are supplied to demonstrate that the proposed algorithm significantly improves the applicability of existing methods and can monitor correlated sensors satisfactorily.",

keywords = "Nonlinear process, particle approximation, sensor monitoring, state estimation, variational Bayesian (VB) inference",

author = "Shunyi Zhao and Shmaliy, {Yuriy S.} and Ahn, {Choon Ki} and Chunhui Zhao",

note = "Funding Information: Manuscript received June 18, 2018; revised November 3, 2018, January 3, 2019, and February 13, 2019; accepted March 5, 2019. Date of publication April 1, 2019; date of current version October 31, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61833007 and Grant 61603155, in part by the 111 Project under Grant B12018, and in part by the National Research Foundation of Korea through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325. (Corresponding authors: Shunyi Zhao; Choon Ki Ahn.) S. Zhao is with the Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China (e-mail:,shunyi.s.y@gmail.com). Publisher Copyright: {\textcopyright} 1982-2012 IEEE.",

year = "2020",

month = mar,

doi = "10.1109/TIE.2019.2907505",

language = "English",

volume = "67",

pages = "2294--2303",

journal = "IEEE Transactions on Industrial Electronics",

issn = "0278-0046",

publisher = "IEEE Industrial Electronics Society",

number = "3",

}

TY - JOUR

T1 - Probabilistic Monitoring of Correlated Sensors for Nonlinear Processes in State Space

AU - Zhao, Shunyi

AU - Shmaliy, Yuriy S.

AU - Ahn, Choon Ki

AU - Zhao, Chunhui

N1 - Funding Information: Manuscript received June 18, 2018; revised November 3, 2018, January 3, 2019, and February 13, 2019; accepted March 5, 2019. Date of publication April 1, 2019; date of current version October 31, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61833007 and Grant 61603155, in part by the 111 Project under Grant B12018, and in part by the National Research Foundation of Korea through the Ministry of Science, ICT, and Future Planning under Grant NRF-2017R1A1A1A05001325. (Corresponding authors: Shunyi Zhao; Choon Ki Ahn.) S. Zhao is with the Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), Jiangnan University, Wuxi 214122, China (e-mail:,shunyi.s.y@gmail.com). Publisher Copyright: © 1982-2012 IEEE.

PY - 2020/3

Y1 - 2020/3

N2 - To optimize control and/or state estimation of industrial processes, information about measurement quality provided by sensors is required. In this paper, a probabilistic scheme is proposed in discrete-time nonlinear state space with the purpose of sensor monitoring. A quantitative index representing the measurement quality, as well as satisfied state estimates, is obtained by estimating the probability density functions (PDFs) of the states and the measurement noise covariance considered as a random variable using the variational Bayesian approach. To solve the intractable integrals of nonlinear PDFs in real time, a set of weighted particles is generated to overlap an empirical density of state, while the PDF of the measurement noise is still derived analytically. An example of localization and an experiment with a rotary flexible joint are supplied to demonstrate that the proposed algorithm significantly improves the applicability of existing methods and can monitor correlated sensors satisfactorily.

AB - To optimize control and/or state estimation of industrial processes, information about measurement quality provided by sensors is required. In this paper, a probabilistic scheme is proposed in discrete-time nonlinear state space with the purpose of sensor monitoring. A quantitative index representing the measurement quality, as well as satisfied state estimates, is obtained by estimating the probability density functions (PDFs) of the states and the measurement noise covariance considered as a random variable using the variational Bayesian approach. To solve the intractable integrals of nonlinear PDFs in real time, a set of weighted particles is generated to overlap an empirical density of state, while the PDF of the measurement noise is still derived analytically. An example of localization and an experiment with a rotary flexible joint are supplied to demonstrate that the proposed algorithm significantly improves the applicability of existing methods and can monitor correlated sensors satisfactorily.

KW - Nonlinear process

KW - particle approximation

KW - sensor monitoring

KW - state estimation

KW - variational Bayesian (VB) inference

UR - http://www.scopus.com/inward/record.url?scp=85074700286&partnerID=8YFLogxK

U2 - 10.1109/TIE.2019.2907505

DO - 10.1109/TIE.2019.2907505

M3 - Article

AN - SCOPUS:85074700286

SN - 0278-0046

VL - 67

SP - 2294

EP - 2303

JO - IEEE Transactions on Industrial Electronics

JF - IEEE Transactions on Industrial Electronics

IS - 3

M1 - 8678661

ER -

Probabilistic Monitoring of Correlated Sensors for Nonlinear Processes in State Space

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this