Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms

Yu Yao Wu; Ying Zhang; Hui Jie Sun; Rui Zhang

doi:10.1109/CCDC49329.2020.9164339

Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms

Yu Yao Wu
, Ying Zhang
, Hui Jie Sun
, Rui Zhang

Harbin Institute of Technology Shenzhen
Shenzhen Polytechnic

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

This paper addresses the problem of sensor fault detection for practical systems based on stochastic gradient identification techniques. Three algorithms are constructed by utilizing the hierarchical identification principle. Different from some existing algorithms, a weighted factor was introduced and a combination of the information in both the last step and the current step can be used to update the estimation of the variables. Due to the use of the latest updated information, the proposed algorithms can achieve better convergence performance than some existing algorithms by appropriately choosing the tuning parameter. A numerical example is given to show the superiority of the presented algorithms.

Original language	English
Title of host publication	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	993-998
Number of pages	6
ISBN (Electronic)	9781728158549
DOIs	https://doi.org/10.1109/CCDC49329.2020.9164339
State	Published - Aug 2020
Externally published	Yes
Event	32nd Chinese Control and Decision Conference, CCDC 2020 - Hefei, China Duration: 22 Aug 2020 → 24 Aug 2020

Publication series

Name	Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

Conference

Conference	32nd Chinese Control and Decision Conference, CCDC 2020
Country/Territory	China
City	Hefei
Period	22/08/20 → 24/08/20

Keywords

Sensor fault
fault detection
stochastic gradient identification
weight factor

Access to Document

10.1109/CCDC49329.2020.9164339

Cite this

Wu, Y. Y., Zhang, Y., Sun, H. J., & Zhang, R. (2020). Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020 (pp. 993-998). Article 9164339 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC49329.2020.9164339

@inproceedings{85c273c971454738b6072e379e0a54ec,

title = "Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms",

abstract = "This paper addresses the problem of sensor fault detection for practical systems based on stochastic gradient identification techniques. Three algorithms are constructed by utilizing the hierarchical identification principle. Different from some existing algorithms, a weighted factor was introduced and a combination of the information in both the last step and the current step can be used to update the estimation of the variables. Due to the use of the latest updated information, the proposed algorithms can achieve better convergence performance than some existing algorithms by appropriately choosing the tuning parameter. A numerical example is given to show the superiority of the presented algorithms.",

keywords = "Sensor fault, fault detection, stochastic gradient identification, weight factor",

author = "Wu, \{Yu Yao\} and Ying Zhang and Sun, \{Hui Jie\} and Rui Zhang",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 32nd Chinese Control and Decision Conference, CCDC 2020 ; Conference date: 22-08-2020 Through 24-08-2020",

year = "2020",

month = aug,

doi = "10.1109/CCDC49329.2020.9164339",

language = "英语",

series = "Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "993--998",

booktitle = "Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020",

address = "美国",

}

Wu, YY, Zhang, Y, Sun, HJ & Zhang, R 2020, Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms. in Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020., 9164339, Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020, Institute of Electrical and Electronics Engineers Inc., pp. 993-998, 32nd Chinese Control and Decision Conference, CCDC 2020, Hefei, China, 22/08/20. https://doi.org/10.1109/CCDC49329.2020.9164339

Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms. / Wu, Yu Yao; Zhang, Ying; Sun, Hui Jie et al.
Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 993-998 9164339 (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms

AU - Wu, Yu Yao

AU - Zhang, Ying

AU - Sun, Hui Jie

AU - Zhang, Rui

PY - 2020/8

Y1 - 2020/8

N2 - This paper addresses the problem of sensor fault detection for practical systems based on stochastic gradient identification techniques. Three algorithms are constructed by utilizing the hierarchical identification principle. Different from some existing algorithms, a weighted factor was introduced and a combination of the information in both the last step and the current step can be used to update the estimation of the variables. Due to the use of the latest updated information, the proposed algorithms can achieve better convergence performance than some existing algorithms by appropriately choosing the tuning parameter. A numerical example is given to show the superiority of the presented algorithms.

AB - This paper addresses the problem of sensor fault detection for practical systems based on stochastic gradient identification techniques. Three algorithms are constructed by utilizing the hierarchical identification principle. Different from some existing algorithms, a weighted factor was introduced and a combination of the information in both the last step and the current step can be used to update the estimation of the variables. Due to the use of the latest updated information, the proposed algorithms can achieve better convergence performance than some existing algorithms by appropriately choosing the tuning parameter. A numerical example is given to show the superiority of the presented algorithms.

KW - Sensor fault

KW - fault detection

KW - stochastic gradient identification

KW - weight factor

UR - https://www.scopus.com/pages/publications/85091562845

U2 - 10.1109/CCDC49329.2020.9164339

DO - 10.1109/CCDC49329.2020.9164339

M3 - 会议稿件

AN - SCOPUS:85091562845

T3 - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

SP - 993

EP - 998

BT - Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 32nd Chinese Control and Decision Conference, CCDC 2020

Y2 - 22 August 2020 through 24 August 2020

ER -

Wu YY, Zhang Y, Sun HJ, Zhang R. Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms. In Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 993-998. 9164339. (Proceedings of the 32nd Chinese Control and Decision Conference, CCDC 2020). doi: 10.1109/CCDC49329.2020.9164339

Sensor Fault Detection Based on Weighted Stochastic Gradient Identification Algorithms

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