An anomaly detection and isolation scheme with instance-based learning and sequential analysis

Tae-Sic Yoo; Humberto E. Garcia

An anomaly detection and isolation scheme with instance-based learning and sequential analysis

Nuclear Science & Technology

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

Abstract

This paper presents an online anonmaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance.

Original language	English
Title of host publication	5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)
Pages	1106-1109
Number of pages	4
State	Published - 2006
Event	5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006) - Albuquerque, NM, United States Duration: Nov 12 2006 → Nov 16 2006

Publication series

Name	5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)
Volume	2006

Conference

Conference	5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)
Country/Territory	United States
City	Albuquerque, NM
Period	11/12/06 → 11/16/06

Cite this

Yoo, T.-S., & Garcia, H. E. (2006). An anomaly detection and isolation scheme with instance-based learning and sequential analysis. In 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006) (pp. 1106-1109). (5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006); Vol. 2006).

Yoo, Tae-Sic ; Garcia, Humberto E. / An anomaly detection and isolation scheme with instance-based learning and sequential analysis. 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006). 2006. pp. 1106-1109 (5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)).

@inproceedings{4aa12255a9d649359a45e2cb1d9c5b5f,

title = "An anomaly detection and isolation scheme with instance-based learning and sequential analysis",

abstract = "This paper presents an online anonmaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance.",

author = "Tae-Sic Yoo and Garcia, \{Humberto E.\}",

year = "2006",

language = "English",

isbn = "0894480510",

series = "5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)",

pages = "1106--1109",

booktitle = "5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)",

note = "5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006) ; Conference date: 12-11-2006 Through 16-11-2006",

}

Yoo, T-S & Garcia, HE 2006, An anomaly detection and isolation scheme with instance-based learning and sequential analysis. in 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006). 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006), vol. 2006, pp. 1106-1109, 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006), Albuquerque, NM, United States, 11/12/06.

An anomaly detection and isolation scheme with instance-based learning and sequential analysis. / Yoo, Tae-Sic ; Garcia, Humberto E.
5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006). 2006. p. 1106-1109 (5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006); Vol. 2006).

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

TY - GEN

T1 - An anomaly detection and isolation scheme with instance-based learning and sequential analysis

AU - Yoo, Tae-Sic

AU - Garcia, Humberto E.

PY - 2006

Y1 - 2006

N2 - This paper presents an online anonmaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance.

AB - This paper presents an online anonmaly detection and isolation (FDI) technique using an instance-based learning method combined with a sequential change detection and isolation algorithm. The proposed method uses kernel density estimation techniques to build statistical models of the given empirical data (null hypothesis). The null hypothesis is associated with the set of alternative hypotheses modeling the abnormalities of the systems. A decision procedure involves a sequential change detection and isolation algorithm. Notably, the proposed method enjoys asymptotic optimality as the applied change detection and isolation algorithm is optimal in minimizing the worst mean detection/isolation delay for a given mean time before a false alarm or a false isolation. Applicability of this methodology is illustrated with redundant sensor data set and its performance.

UR - https://www.scopus.com/pages/publications/34047173186

M3 - Conference contribution

AN - SCOPUS:34047173186

SN - 0894480510

SN - 9780894480515

T3 - 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)

SP - 1106

EP - 1109

BT - 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)

T2 - 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)

Y2 - 12 November 2006 through 16 November 2006

ER -

Yoo TS , Garcia HE. An anomaly detection and isolation scheme with instance-based learning and sequential analysis. In 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006). 2006. p. 1106-1109. (5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006)).

An anomaly detection and isolation scheme with instance-based learning and sequential analysis

Abstract

Publication series

Conference

Other files and links

Fingerprint

Cite this