Detection and Identification of Faulty Sensors with Maximized Sensitivity

S. Joe QIN; Weihua LI

doi:10.1109/acc.1999.782901

Detection and Identification of Faulty Sensors with Maximized Sensitivity

^*Corresponding author for this work

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

11 Citations (Scopus)

Abstract

In this paper we propose a new method for the detection, identification and reconstruction of faulty seniors using a generalized normal process model. The model residual is used to detect sensor faults, and a structured residual approach with maximized sensitivity (SRAMS) is proposed to identify the faulty sensor. An exponentially weighted moving average (EWMA) filter is applied to reducing the effects of noise and dynamic transients. Three different indices are proposed and compared for the identification of faulty sensors. Faulty sensor is reconstructed based on the normal process model and faulty data. The effectiveness of the proposed scheme is tested using the data from an industrial boiler process, where four types of faults are simulated.

Original language	English
Title of host publication	Proceedings of the 1999 American Control Conference
Publisher	Institute of Electrical and Electronics Engineers
Pages	613-617
Number of pages	5
ISBN (Electronic)	0780349938
ISBN (Print)	0780349903, 0780349911
DOIs	https://doi.org/10.1109/acc.1999.782901
Publication status	Published - Jun 1999
Externally published	Yes
Event	1999 American Control Conference (99ACC) - San Diego, United States Duration: 2 Jun 1999 → 4 Jun 1999

Publication series

Name	Proceedings of the American Control Conference
Publisher	Institute of Electrical and Electronics Engineers
ISSN (Print)	0743-1619
ISSN (Electronic)	2378-5861

Conference

Conference	1999 American Control Conference (99ACC)
Country/Territory	United States
City	San Diego
Period	2/06/99 → 4/06/99

Access to Document

10.1109/acc.1999.782901

Cite this

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title = "Detection and Identification of Faulty Sensors with Maximized Sensitivity",

abstract = "In this paper we propose a new method for the detection, identification and reconstruction of faulty seniors using a generalized normal process model. The model residual is used to detect sensor faults, and a structured residual approach with maximized sensitivity (SRAMS) is proposed to identify the faulty sensor. An exponentially weighted moving average (EWMA) filter is applied to reducing the effects of noise and dynamic transients. Three different indices are proposed and compared for the identification of faulty sensors. Faulty sensor is reconstructed based on the normal process model and faulty data. The effectiveness of the proposed scheme is tested using the data from an industrial boiler process, where four types of faults are simulated.",

author = "QIN, {S. Joe} and Weihua LI",

year = "1999",

month = jun,

doi = "10.1109/acc.1999.782901",

language = "English",

isbn = "0780349903",

series = "Proceedings of the American Control Conference",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "613--617",

booktitle = "Proceedings of the 1999 American Control Conference",

note = "1999 American Control Conference (99ACC) ; Conference date: 02-06-1999 Through 04-06-1999",

}

QIN, SJ & LI, W 1999, Detection and Identification of Faulty Sensors with Maximized Sensitivity. in Proceedings of the 1999 American Control Conference. Proceedings of the American Control Conference, Institute of Electrical and Electronics Engineers, pp. 613-617, 1999 American Control Conference (99ACC), San Diego, California, United States, 2/06/99. https://doi.org/10.1109/acc.1999.782901

Detection and Identification of Faulty Sensors with Maximized Sensitivity. / QIN, S. Joe; LI, Weihua.
Proceedings of the 1999 American Control Conference. Institute of Electrical and Electronics Engineers, 1999. p. 613-617 (Proceedings of the American Control Conference).

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

TY - GEN

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AU - LI, Weihua

PY - 1999/6

Y1 - 1999/6

N2 - In this paper we propose a new method for the detection, identification and reconstruction of faulty seniors using a generalized normal process model. The model residual is used to detect sensor faults, and a structured residual approach with maximized sensitivity (SRAMS) is proposed to identify the faulty sensor. An exponentially weighted moving average (EWMA) filter is applied to reducing the effects of noise and dynamic transients. Three different indices are proposed and compared for the identification of faulty sensors. Faulty sensor is reconstructed based on the normal process model and faulty data. The effectiveness of the proposed scheme is tested using the data from an industrial boiler process, where four types of faults are simulated.

AB - In this paper we propose a new method for the detection, identification and reconstruction of faulty seniors using a generalized normal process model. The model residual is used to detect sensor faults, and a structured residual approach with maximized sensitivity (SRAMS) is proposed to identify the faulty sensor. An exponentially weighted moving average (EWMA) filter is applied to reducing the effects of noise and dynamic transients. Three different indices are proposed and compared for the identification of faulty sensors. Faulty sensor is reconstructed based on the normal process model and faulty data. The effectiveness of the proposed scheme is tested using the data from an industrial boiler process, where four types of faults are simulated.

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U2 - 10.1109/acc.1999.782901

DO - 10.1109/acc.1999.782901

M3 - Conference paper (refereed)

SN - 0780349903

SN - 0780349911

T3 - Proceedings of the American Control Conference

SP - 613

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BT - Proceedings of the 1999 American Control Conference

PB - Institute of Electrical and Electronics Engineers

T2 - 1999 American Control Conference (99ACC)

Y2 - 2 June 1999 through 4 June 1999

ER -

Detection and Identification of Faulty Sensors with Maximized Sensitivity

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